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版本更新 · v4arXiv:2603.10302

How to make the most of your masked language model for protein engineering

Calvin McCarter, Nick Bhattacharya, Sebastian W. Ober, Hunter Elliott

原文摘要

A plethora of protein language models have been released in recent years. Yet comparatively little work has addressed how to best sample from them to optimize desired biological properties. We fill this gap by proposing a flexible, effective sampling method for masked language models (MLMs), and by systematically evaluating models and methods both in silico and in vitro on actual antibody therapeutics campaigns. Firstly, we propose sampling with stochastic beam search, exploiting the fact that MLMs are remarkably efficient at evaluating the pseudo-perplexity of the entire 1-edit neighborhood of a sequence. Reframing generation in terms of entire-sequence evaluation enables flexible guidance with multiple optimization objectives. Secondly, we report results from our extensive in vitro head-to-head evaluation for the antibody engineering setting. This reveals that the choice of sampling method can have a substantial impact, motivating future research into this under-explored area.

cs.LG · q-bio.QM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01543

GSExplorer: Interactive World Generation with a Persistent 3D Gaussian World State

Sicheng Yu, Liu Liu, Xinjie Wang, Xinjiang Wang, Taojun Ding, Wenhao Hu, Beizhen Zhao, Puxin Yan 等 10 位作者

原文摘要

Interactive world generation requires both accurate local control and stable scene consistency over long rollouts. Existing methods improve local generation through camera or geometry conditioning and extend historical context with memory mechanisms. However, long-horizon exploration remains challenging as target-view guidance becomes incomplete and inconsistencies accumulate across repeated scene updates. We present \textbf{GSExplorer}, a generation--reconstruction framework built around a persistent 3D Gaussian world state. At each step, the current 3DGS scene provides multimodal rendering conditions for a condition-driven video generator, while camera geometry is injected through a separate Relative Ray Encoding branch. Generated videos are then fed back to a SLAM-based reconstruction backend, where inconsistency filtering prevents conflicting content from corrupting the scene and cross-chunk bundle adjustment improves spatial alignment across generation steps. We further design a data-construction pipeline that reproduces realistic 3DGS degradation during scene extrapolation, yielding 293K training pairs of degraded multimodal renderings and real target videos. GSExplorer achieves the best overall performance among the compared methods on both single-step and three-step WorldScore evaluation, with strong camera control and consistency across both settings, including a Camera Control score of 99.07 in single-step extrapolation. On 481-frame leave-and-return rollouts, it also shows strong revisit consistency, demonstrating the value of maintaining a reliable 3D world state for continued exploration.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.12830

Cross-Behavior Policy Optimization: Unified Learning from Offline Demonstrations and On- and Off-Policy Rollouts via a Shared Regression Target

Du Cheng, Junyu Lu, Jingfan Zhang, Yuzhuo Zhang, Haoxuan Jia, Shichao Weng

原文摘要

Reasoning post-training draws on offline demonstrations and policy-generated rollouts, yet conventional supervised fine-tuning (SFT) followed by reinforcement learning (RL) separates their objectives and limits continued reuse of collected responses. We propose \textbf{Cross-Behavior Policy Optimization} (CBPO), which makes these sources compatible with one regression loss through a fixed-reference target. Because the generating policy does not enter this target, positive and negative offline completions, reference responses, and current and historical rollouts can share a common reward-labeled record format and replay buffer. As new responses enter the buffer, optimization retains the same regression objective without coordinating separate SFT and RL objectives or requiring stored behavior-policy probabilities, importance weighting, or a learned critic. Restricting the available sources gives offline, on-policy, and off-policy variants, while CBPO-Unified combines them. Across two model scales and four mathematical reasoning benchmarks, CBPO delivers leading \texttt{avg@32} and \texttt{pass@32} performance across benchmarks and data regimes. On Qwen3-1.7B, CBPO-Unified improves benchmark-averaged \texttt{avg@32} and \texttt{pass@32} over SFT DAPO by 2.33 and 4.44 percentage points, respectively. In the replay ablation, retaining historical rollouts improves these metrics by 1.87 and 3.72 percentage points over the no-history configuration.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01541

MoPort: A View-Aware 2D Interface for Motion Language Models

Lixing Tan, Qing Xia, Yuting Guo, Shuai Li, Aimin Hao

原文摘要

Motion language models (MoLMs) encode 3D human motion as discrete tokens for language-based understanding. Deploying them on monocular video raises two problems. Pose detection yields 2D joint sequences, leaving a dimensional gap from the 3D motion used in training. Moreover, the 2D observation changes with the camera viewpoint, while the depth lost in projection makes the 2D pose ambiguous across viewpoints. We propose MoPort, a view-aware 2D interface that enables MoLMs to take 2D observations as input. We introduce Paired View Alignment (PVA), which gives two views of the same motion a shared 3D teacher target and directly constrains the consistency of their latents, so that 2D observations from different viewpoints are mapped to a consistent semantic representation. Camera-Modulated Encoding (CME) estimates the camera extrinsics from the 2D sequence and uses them to modulate encoding, providing the 2D latents with geometric information about the viewpoint. MoPort achieves R@1 of 0.5674 on HumanML3D and 0.3286 on KIT-ML, outperforming all evaluated baselines. On HumanML3D, its R@1 decreases only from 0.5763 on in-distribution viewpoints to 0.5585 on out-of-distribution viewpoints. We validate portability on three downstream MoLMs. Furthermore, on the real-world RGB video dataset Fit3D, MoPort achieves 80.36\% accuracy. Code will be released upon acceptance.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01539

The Structure of Disagreement in Emotion Annotation

Emily Öhman, Anna Koufakou, Eugenia Diegoli

原文摘要

Calculating inter-annotator agreement is an established practice in NLP, and disagreement-aware work increasingly retains the distribution of human judgments rather than reducing annotations to a single gold label. Keeping the distribution, however, does not explain why annotators disagree. This paper examines the types of disagreement that occur in emotion annotation, starting from multilabel annotations of 1,677 sentence-level units from Ernest Hemingway's The Old Man and the Sea and testing the patterns they reveal against individual-level annotations from BRIGHTER and GoEmotions. Across the three datasets, we find three main recurring disagreement structures. Emotion categories overlap systematically; annotators differ both in how broadly they apply labels and in how they treat particular category boundaries, while sparse participation and explicit abstention can look like disagreement. Where intensity is rated, disagreement over whether an emotion is present can be separated from disagreement over its intensity once recognized. Similar aggregate disagreement can therefore arise from qualitatively different annotation processes. Hard aggregation removes most of this structure, while annotator vote-share representations keep marginal label frequencies but not the co-occurrence, annotator-specific, or procedural patterns that produced them. We argue that disagreement should be understood in terms of its underlying patterns before it is summarized, aggregated, or used as a modeling target, and propose a vocabulary for disagreement structure.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01537

Task-driven sparse-view planar X-ray computed tomography for axial metrology

Yibin Tian, Shuming Yang, Wolfgang Heidrich

原文摘要

Computational imaging can reduce the acquisition burden when the system is designed around task-relevant information rather than complete image recovery. We investigate this framework for sparse-view planar X-ray tomography (XCT) of layered objects, using measurement of the axial termination gap (ATG) of back-drilled printed-circuit-board (PCB) vias as a representative task. Instead of using 3D reconstruction, the proposed method estimates ATG directly from polychromatic projection data through a physics-based sensitivity model. A gated hierarchical estimator first fits ATG together with radiometric perturbations and selectively introduces global geometry and specimen-warp models only when a reduced chi-squared adequacy test and Bayesian information criterion justify the added degrees of freedom. The same low-dimensional perturbation model is incorporated into marginalized Fisher-information analyses, yielding task-specific conditional and calibration-aware Cram'er-Rao bounds. Numerical experiments show that, under a fixed total photon budget, task-driven metrology changes little over 12-64 views, while reconstruction-driven metrology is strongly view-dependent with much larger errors. The direct estimator also follows the expected photon-limited scaling. These results demonstrate a computational sensing strategy in which XCT acquisition, physical modeling, and inference are evaluated according to the quantitative information required by the task rather than for general-purpose volumetric image fidelity.

eess.IV · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01534

AbacusGS: Gaussian Splatting without Per-Splat Sorting

Jiacheng Ding, Yanliang Qi, Cong Guo, Peihua Mai

原文摘要

3D Gaussian Splatting sorts every splat by depth in every frame. Viewers that draw splats with the hardware rasterizer and sort them on the CPU, as most web and mobile viewers do, spend most of the frame time in this sort. We present AbacusGS, which stores the blending order of a 3DGS scene in the scene layout. AbacusGS groups the splats into octree boxes of at most 128 splats and stores each box in order along an axis chosen at write time. In each frame, AbacusGS sorts only the visible boxes, and one dot product per box decides whether the box is read forward or backward. A short order-aware fine-tuning converts an existing 3DGS model, and the over operator of 3DGS stays unchanged. On 13 standard scenes, AbacusGS loses 0.13 dB PSNR on average against an equally fine-tuned 3DGS model. In an OpenGL viewer that orders splats on the CPU, AbacusGS renders frames 4.5 times faster than per-splat sorting. We also present a compact format of 17 bytes per splat and a CUDA tile renderer that use the same order. The code will be released.

cs.CV · cs.GR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01530

Learning Gradients of Homogeneous Functions

Mayur S. Gowda, Srinivasa Pranav, Shreyas Chaudhari, José M. F. Moura

原文摘要

Homogeneous gradient functions have widespread significance, including applications in maximum energy subspace selection and retrieval problems. Euler's homogeneous function theorem specifies how the potential associated with a positively k-homogeneous gradient field can be recovered directly from the field itself. We use Euler's theorem to build on GradNets and propose a homogeneous gradient network architecture that is guaranteed to correspond to the gradient of a homogeneous function. Then, we describe how monotone homogeneous GradNets universally approximate the gradients of k-homogeneous convex functions. The inductive bias of homogeneous GradNets enables them to outperform baseline ICNNs by up to 7dB in our max energy subspace projection experiment and by up to 13% higher accuracy, 98% fewer parameters, and 70% fewer FLOPS in our amortized inner product search experiment.

cs.LG · eess.SP · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01529

AR-RRG: Anatomy-Guided Retrieval-Driven Radiology Report Generation

Xiang Zhang, Shijie Song, Wanqing Zhao, Wei Zhou, Rui Cao, Dekui Wang, Qiyao Hu, Xianlin Peng 等 11 位作者

原文摘要

Radiology report generation (RRG) streamlines clinical workflows and alleviates the burden of radiologists' documentation. Standard vision-language models capture global visual cues and lack fine-grained alignment between anatomical regions and diagnostic text. Most retrieval-augmented methods retrieve complete historical reports, introducing redundant noise and severe vision-text mismatches that trigger clinically risky hallucinations and missed lesions. In this paper, we introduce AR-RRG, an anatomy-guided retrieval-driven method for the generation of radiology reports. AR-RRG consists of two core modules: the anatomy-aware self-training lesion detection network (ASLDNet) and the retrieval-driven radiology report generation (RRRG). ASLDNet leverages anatomy-aware spatial verification (ASV) and class-adaptive dynamic thresholding (CDT) via self-training, delivering accurate lesion localization to support subsequent generation. As the generative branch of AR-RRG, RRRG eliminates clinical hallucinations through dual-granularity retrieval (DR) and complements missing lesions with a neighborhood consensus (NC) mechanism. A parallel continuous visual perception (PCVP) stream converts radiographs into visual tokens through fixed medical encoding and resampling. Multi-scale text and visual features are fused, followed by LoRA-efficient large language model tuning to generate clinically consistent, visually grounded radiology reports. Extensive evaluations on MS-CXR, MIMIC-CXR, and IU X-Ray demonstrate that AR-RRG consistently outperforms state-of-the-art methods across mainstream metrics. Our code will be made publicly available.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01527

From Tool State to Scientific Action: Auditing Decision Provenance in Biomedical Research Agents

Xingyu Yi, Xinya Zhang, Yunshen Zhou, Haoyu Bao

原文摘要

A scientific agent can give the same verdict while reading and citing different evidence. BioAgent-DF tests this separation using paired reference and candidate values saved at the same scientific computation site. It restores or transplants those values at their original coordinates, then runs the agent on healthy hardware. Across eight SciFact batches, Qwen3.5-4B shows repeatable citation changes in three capture-claim combinations, and Gemma 3 4B IT shows repeatable read and citation changes in four. These seven combinations cover two claims. Full restoration recovers the reference evidence sets while verdicts remain unchanged. Verdict changes observed under the baseline prompt did not recur under the explicit-example prompt. Complementary tool and optimizer experiments distinguish recovery of numerical state, recovery of ranked lists, and restoration of an agent's visible history. These results show that an audit should track both the evidence an agent uses and its final answer. In hERG cardiac-safety assay allocation, silent numerical faults changed testing decisions while the fixed-budget shortlist kept the same number of compounds labeled as blockers. After the agent had used faulty values, restoring its earlier execution state and supplying certified scores recovered all 39 changed decisions, whereas replacing the prediction file recovered none; no incorrect action was committed. A streaming implementation of the arithmetic checks reduced the total time for forward execution and verification by 42.4% relative to the original FP64 implementation, with identical flags and recovery outputs.

cs.AI · cs.LG · q-bio.QM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01525

InformedXRD: Reproducible Benchmarks and Physics-Informed Evaluation for Powder Diffraction Symmetry Classification

Edward G Friedman, Elizabeth J. Baggett, Abhishek Shetty, Derrick Chan-Sew, Vanellsa Acha, Harshitha Dwaracherla, P. A. Kienzle, William Ratcliff

原文摘要

Machine learning for powder diffraction (PXRD) symmetry classification is being trained at scale, yet the field lacks a common evaluation standard: papers report results on different subsets of experimental archives, with different target taxonomies, preprocessing, and metrics, and published models are rarely released in a form that permits rescoring. We present InformedXRD, an evaluation framework with three parts: (i) a machine-readable mapping from 230 space groups to 99 extinction groups, the finest classes powder diffraction can identify from systematic absences alone; (ii) a benchmark-construction method whose inclusion rules are specified as code and reproduce from a frozen snapshot, instantiated as RRUFF-473, RRUFF-325, and, as a small example, opXRD-98; (iii) a reporting protocol with prior-only frequency baselines, a label-permutation test, a hierarchy-aware error metric on the condensed translationengleiche subgroup graph, and stratified evaluation. We build on the mapping, RRUFF benchmarks, subgroup graph, and four ViT checkpoints released in our earlier work (Baggett et al., 2026). Using these checkpoints, a 1D residual CNN, and six architectures trained under the XRDBench recipe (Cao et al., 2025), we show that evaluation design changes what conclusions are justified: paired tests point in different directions at Top-1 and Top-5; paired Top-k tests do not separate the three leading XRDBench models, while hierarchy-aware analysis describes error distance and direction across all six; an input-blind frequency predictor matches the models on aggregate Top-k, yet a label-permutation test still detects pattern-specific evidence; and stratified analysis exposes a label-entropy confound that aggregate accuracy hides. All mapping tables, benchmark code, scripts, and checkpoints are released as shared infrastructure for PXRD-ML evaluation.

cs.LG · cond-mat.mtrl-sci

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01521

AI-Aided and AI-Oriented Integrated Sensing, Communication, and Computation: A Survey

Yaya Etiabi, Wessam Ajib, El Mehdi Amhoud, Halima Elbiaze

原文摘要

The evolution from 5G to 6G networks marks a transition toward the "intelligence of everything." While 5G enables enhanced connectivity, 6G aspires to embed intelligence directly into communication infrastructures, creating systems that are adaptive, context aware, and capable of autonomous decision making. Realizing such systems depends on harmonizing three fundamental functions that compete for shared network resources and often pursue conflicting goals: (i) sensing for environmental and contextual information acquisition, (ii) communication for reliable and timely information exchange, and (iii) computation for information processing and decision making. This harmonization necessitates shifting from independent optimization to their joint design as a unified system, a paradigm known as Integrated Sensing, Communication, and Computation (ISCC). In parallel, AI has emerged both as an enabling tool for ISCC optimization and as a workload whose requirements influence ISCC system design. This survey comprehensively examines ISCC from this dual perspective by distinguishing between AI-aided ISCC, in which learning-based methods support the joint design and optimization of sensing, communication, and computation, and AI-oriented ISCC, in which these functions are jointly configured to support learning, inference, and distributed intelligence. The survey further examines the co-design of AI and ISCC, where AI requirements and ISCC constraints jointly shape system and model decisions. It further provides a comparative synthesis of the resulting design approaches, system architectures, performance metrics, and evaluation practices, relates these developments to application-specific requirements across different domains, and identifies open research problems toward scalable, adaptive, and trustworthy AI-ISCC systems.

eess.SP · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01514

Federated Vector Retrieval across Heterogeneous VectorDBs

Mintaek Lim, Sieun Hyeon, Geon Chio, Jaeyoung Do

原文摘要

Vector databases are now core infrastructure for AI retrieval systems, yet many real deployments must serve queries over multiple independently constructed vectorDBs built with different embedding models. This creates a systems problem of federated retrieval over heterogeneous vectorDBs: vectors and similarity scores are not directly comparable across embedding spaces, while fully resolving this incompatibility through corpus-wide re-embedding and re-indexing is often operationally prohibitive. Existing methods therefore face a difficult trade-off between retrieval quality, construction cost, and query throughput. We present HVMap, a lightweight compatibility layer for federated retrieval across heterogeneous vectorDBs. Instead of rebuilding databases into a unified space, HVMap learns bidirectional mappings between embedding spaces using a fuzzy contrastive loss, enabling calibrated cross-database scoring without corpus migration or index reconstruction. HVMap is system-agnostic and further scales to multi-vectorDB settings while preserving ranking fidelity through a hub-based architecture. We evaluate HVMap in open-domain, cross-domain, and scale-out settings, and also construct a new benchmark for heterogeneous cross-domain federation. Across these settings, HVMap consistently achieves a better quality--cost--throughput trade-off than baselines, achieving up to 3.59X higher nDCG@10, up to 2.01X higher query throughput, and construction time as low as 0.3% of baseline methods. These results show that HVMap makes heterogeneous vectorDB federation practical at scale.

cs.DB · cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01513

Label-Free Localization and Test-Time Suppression of CLIP's Typographic Circuits

Bohan Liu, Wenqian Ye, Guangzhi Xiong, Zhenghao He, Sanchit Sinha, Aidong Zhang

原文摘要

CLIP-based Vision-Language Models have become cornerstones of modern multimodal systems. The cross-modal contrastive objective equips the CLIP vision encoder to both see objects and read text. These two abilities induce an entanglement between the encoding of lexical and semantic shapes, creating a distinctive vulnerability to typographic attacks, in which rendered text overrides an image's semantic content with its literal meaning. The black-box nature of these encoders has prevented a mechanistic interpretation of such entanglement. We introduce a head-decomposed Sparse Dictionary Learning (SDL) architecture that, by design, decomposes individual attention heads into interpretable, head-specific latents. Based on this inductive disentanglement, we re-purpose Clarity Score and propose normalized Text Attribution Score, which are both label-free, to identify attention heads that carry typographic information. As a causal validation of successful circuit separation, we perform a mechanistic intervention that reweighs attention away from the patch tokens in the identified lexical heads at test-time. Experiments show that the intervention effectively recovers object classification under typographic attack and, across five CLIP variants and four typographic benchmarks, achieves up to 46.9% improvement while incurring up to 1% accuracy loss in general image perception, outperforming prior supervised and training-free methods. Expanding on the mechanistic intervention, we propose a test-time adaptive attention head routing to further reduce the impact on performance at matched robustness, pushing beyond the Pareto frontier of current robustness-perception tradeoff.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01512

Abstention Spends Its Budget on Local Label Variance More Than on Novelty: Auditing Conformal Calibration and Selective Prediction under Molecular Scaffold Shift

Arnav Srivastava, Krishna Kassetty

原文摘要

Selective prediction with split conformal calibration promises a post-hoc fix for molecular property prediction: abstain on the least reliable compounds, ideally out-of-distribution chemistry, and the retained set becomes trustworthy without retraining. We audit this recipe for a gradient-boosted fingerprint model on lipophilicity (regression) and BBBP (classification) under a Bemis-Murcko scaffold split, holding the fitted model and evaluation molecules fixed while varying only the calibration fold (18 replicates). (i) On lipophilicity, coverage depends on calibration provenance: calibrating on held-out training-scaffold molecules falls below nominal (PICP 0.841 at a 0.90 target), while novel-scaffold calibration recovers it (0.904); on BBBP, coverage stays above nominal in every regime. (ii) For split conformalized quantile regression (CQR) the conformal correction is a constant, so conformalization does not change what is abstained on. (iii) The abstention score (CQR width; effectively ensemble variance on BBBP) tracks local label variance more than chemical novelty: controlling for local label variance largely explains away the novelty association (partial Spearman 0.067 and 0.047). (iv) At a fixed assay budget, gating on interval width or ensemble variance never improves hit precision over ranking by point prediction, at the pre-specified operating point or across 20 exploratory settings; a plain novelty gate shows small, uncorrected gains at the smallest budgets. (v) In an exploratory analysis, exact conformal selection exceeds its false discovery rate target under training-scaffold calibration (0.303 at q=0.2) and is consistent with it under novel-scaffold calibration, contributing a data-driven batch size rather than a better ranking. Conformalization earns its keep through guarantees whose validity hinges on calibration provenance, not by choosing what to abstain on.

cs.LG · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01511

OpenULF: A Modular Toolkit Enables Data-Driven Ultra-Low-Field MRI with both Classical and Quantum Sensors

Hanhan Wu, Yuchen Yuan, Weiwen Jiang

原文摘要

Ultra-low-field (ULF) Magnetic Resonance Imaging (MRI) offers a portable, low-cost alternative to conventional scanners by operating at field strengths below 10 mT. At these fields, two sensor technologies compete: quantum SQUID (Superconducting Quantum Interference Device) sensors, which provide high sensitivity but require cryogenic cooling, and classical induction coils, which are inexpensive and room-temperature but produce weaker signals. With conventional physics-based imaging, both suffer from low signal-to-noise ratios (SNR). Data-driven reconstruction has overcome similar limits in related inverse problems, but its use for ULF-MRI has been hindered by a lack of open datasets, benchmarks, and flexible simulators. To close this gap, we present OpenULF, a modular, open-source simulation toolkit that generates paired training data and supports end-to-end machine-learning (ML) reconstruction. OpenULF integrates a Bloch-equation-validated physics simulator, calibrated SQUID and coil receiver models, reconstruction ML networks, and an automated design-space exploration engine. Architecturally, a receiver abstract base class, a typed HDF5 contract that decouples physics from learning, and a resumable sweep engine make sensors, models, and datasets independently substitutable. Using OpenULF, we train 774 models across four anatomical datasets and conduct three design-space explorations. The effectiveness of ML reconstruction depends strongly on the dataset and the sensor. We identify a SQUID-to-coil crossover near 450 $μ$T and demonstrate that sensor hardware and ML model capacity can be traded off during system design. Sensor and reconstruction choices must therefore be co-designed: the best sensor is set by the dataset and field strength, not by SNR.

eess.IV · cs.CV · physics.med-ph

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.06388

Humans' ALMANAC: A Human Collaboration Dataset of Action-Level Mental Model Annotations for Agent Collaboration

Jiaju Chen, Yuxuan Lu, Jiayi Su, Chaoran Chen, Songlin Xiao, Zheng Zhang, Yun Wang, Yunyao Li 等 13 位作者

原文摘要

Recent advances in LLM agents have enabled complex cognitive capabilities, such as multi-step reasoning, planning, and tool use, that increasingly position these agents as collaborative partners in human workflows. Effective collaboration, however, requires collaborators to continuously maintain and align mental models of their own reasoning, partners' intentions, and shared goals during the collaborative process. Today's agents rarely develop such capabilities since they are primarily optimized for task completion rather than for maintaining the underlying mental models required for collaborative processes. Moreover, the community lacks authentic human collaboration data with action-level mental model annotations that could guide agents toward collaborative competence. To bridge this gap, we present ALMANAC, a dataset of Action-Level Mental model ANnotations for Agent Collaboration built from the Map Task, a classic dyadic routing task from social science. ALMANAC contains 2,987 collaboration actions, each paired with theory-informed mental model annotations that record the participants' self-reasoning, perceived partner intent, and perceived team goal. We benchmark six LLMs on predicting humans' next-turn behavior and mental models. Our results demonstrate ALMANAC's utility in evaluating models' ability to simulate human collaborative behaviors and infer their underlying mental models. The project page is available at https://human-agent-collab.hailab.io/almanac.

cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01500

Improved Sample Complexity Analysis For First Order Bilevel Reinforcement Learning

Mudit Gaur, Naman Saxena, Vaneet Aggarwal

原文摘要

Bilevel reinforcement learning optimizes an upper-level objective through the policy response to a lower-level reinforcement learning problem. Such problems arise when learning a reward model while adapting a policy to that reward. However, a gap remains between sample-complexity guarantees and practical actor--critic training. Tabular analyses restrict applicability to finite spaces, while continuous-state analyses often impose a uniform Polyak--Łojasiewicz (PL) condition. We show that this condition need not hold even for smooth positive policies with exactly representable values. We develop a single-loop, fully first-order method for continuous states and finite actions with specified multilayer neural actors and a persistently trained nonlinear critic built from fixed features. Under explicit initialization, regularity, feature-excitation, and value-realizability conditions, fixed reference-KL regularization yields local curvature, and we prove containment of the coupled iterates. Shared trajectories control sampling noise, while paired critic analysis controls normalized critic-error differences as the response perturbation shrinks. A joint descent and tracking argument establishes an improved $\widetilde{\mathcal O}(\varepsilon^{-2})$ sample complexity for $O(\varepsilon)$ expected squared gradient norm of the upper objective induced by the selected regularized policy response, including critic training.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01498

FedUDG: Federated Domain Generalization with Unlabeled Clients

Junyu Ren, Wensheng Gan, Philip S. Yu

原文摘要

Federated domain generalization (FDG) aims to learn models that generalize reliably to out-of-distribution (OOD) environments without centralizing client's data. However, existing FDG methods assume that all source clients have labeled data, limiting their ability to leverage the abundant unlabeled client data available in practice. To address this limitation, we propose FedUDG. At the federation level, FedUDG utilizes a parameter-functional differentiation strategy that maintains a semantic anchor updated solely using ground-truth labels. The remaining parameters absorb knowledge from unlabeled domains under bounded aggregation, preventing domain-biased noise from eroding the class semantics. Within each unlabeled client, FedUDG utilizes a student-teacher framework with two complementary objectives: (1) semantic self-training on high-confidence samples, using the functional differentiation between the semantic anchor and classifier to constrain the model to learn semantically pure and cross-domain stable features; and (2) label-free representation distillation on all unlabeled samples to stabilize learning under appearance variations. Together, these objectives guide the model toward a transferable feature space, while mitigating overfitting to domain-specific characteristics. Furthermore, to address the lack of systematic evaluation of FDG under formal privacy constraints, we develop a sample-level differentially private extension, DP-FedUDG, to quantify the trade-off between privacy and OOD generalization. Across four evaluation protocols, FedUDG consistently outperforms state-of-the-art baselines. The code is available at https://github.com/Jr10426/FedUDG

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01496

Sweep Less, Steer More: Using Activation Steering Geometry for Reduced Hyperparameter Search

Nishkal Hundia, Swastik Agrawal, Navita Goyal, Sarah Wiegreffe

原文摘要

Representation steering methods like difference-in-means (DIM) offer a lightweight approach to controlling LLM behavior, but their effectiveness varies widely across concepts, datasets, and layers, making principled deployment costly without exhaustive hyperparameter sweeps. We build on the discriminability index $d'$, a compute-cheap geometric measure of how well contrastive training activations separate along the steering direction. Across five behaviors on 3 models, we demonstrate uses beyond its established role as a dataset-level steerability predictor. First, in the multiple-choice question answering (MCQA) setting, we show that $d'$ reliably identifies performant layers at which to steer, eliminating the need for a layer-wise sweep. Second, we extend this analysis to open-ended multi-token generation, and find that $d'$ remains successful for layer selection across different settings. Lastly, we test whether the position of a generated token's representation on the DIM line predicts steering success on that individual example. In the MCQA setting, this works for high-$d'$ datasets but is inconsistent across behaviors, models, and training-token choices in open-ended generation. This highlights that methods developed and conclusions drawn in single-token MCQA settings do not necessarily transfer to open-ended generation.

cs.LG · cs.AI · cs.CL

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新论文 · v1arXiv:2611.01493

TopoSteer: Topological Layer Selection for Activation Steering in LLMs

Tuc Nguyen, Marcos Gutiérrez-del-Pozo, Eduardo Paluzo-Hidalgo, Yifan Hu, Adaku Uchendu, Thai Le

原文摘要

Activation steering controls large language models at inference time by adding a vector to their internal representations. Most work asks how to build that vector, and far less attention has gone to where to add it, even though steering at a poorly chosen layer can be worse than not steering at all. Prior selectors choose a layer without ever steering the model to test it: they score how separable the target behavior is at that layer, and how consistently the individual examples point along the steering direction. We argue that what matters is not the separability of the two activation clouds, nor the alignment of individual displacements with their mean, but the extent of the cloud of per-example displacements that mean-difference steering averages. We propose a topology-based layer selection method, called TopoSteer, which scores a layer by the total H_0 persistence of its displacement cloud, computed as the length of the cloud's Euclidean minimum spanning tree. We show that the score focuses on the displacement cloud's relative geometry, while class separation affects it only through standardization. We also provide a theoretical explanation for our method by proving that, as example-level variation increases, TopoSteer and discriminability move in opposite directions, so the two criteria can rank layers in opposite ways. Across multiple language models and steering tasks, TopoSteer provides a principled alternative to discriminability- and separability-based layer selection and yields more effective steering without requiring held-out validation.

cs.CL · cs.LG

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新论文 · v1arXiv:2611.01491

Spectral-Convolutional Factorized Transformer for Joint Multipath Parameter Estimation: Applications to mmWave Positioning and Radio SLAM

Yaya Etiabi, Wessam Ajib, Halima Elbiaze

原文摘要

Future 6G wireless networks are expected to evolve toward AI-native systems that support intelligent, environment-aware services, including positioning and mapping. Current research has already integrated AI into several aspects of communication, particularly wireless channel estimation. However, most learning-based channel estimators focus on communication-oriented objectives rather than explicitly estimating multipath parameters such as delay, angle of arrival (AoA), and angle of departure (AoD). These parameters provide essential geometric information for downstream tasks including positioning and radio simultaneous localization and mapping (SLAM). In this work, we propose a Spectral-Convolutional Factorized Transformer (SCFT) for joint multipath component (MPC) estimation from noisy complex channel estimates and physical beam-pair descriptors. SCFT combines local spectral convolution, permutation-equivariant beam-set attention, and a query-based Transformer decoder to detect and classify line-of-sight (LoS) and non-line-of-sight (NLoS) paths and estimate their observed delays, AoAs, and AoDs. Experiments on realistic ray-tracing data show that SCFT reduces the generalized optimal sub-pattern assignment (GOSPA) error by 13.4% compared with ResNet, the strongest competing baseline. We further demonstrate this advantage in independent snapshot positioning and radio SLAM, where SCFT reduces position RMSE by 18.5% and 65.0%, respectively, relative to ResNet. These findings highlight the importance of explicitly recovering multipath geometry, as improvements in MPC estimation can translate into substantial gains in downstream tasks.

eess.SP · cs.AI

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新论文 · v1arXiv:2611.01489

CCD: Context-Calibrated Open-Entity Out-of-Distribution Detection in In-Context Tabular Foundation Models

Zhangkai Wu, Xu Zhang, Zhihao Hao, Zhihong Cui, Yongmin Yoo, Xianxun Zhu, Kaize Shi

原文摘要

In-context tabular foundation models (TFMs), such as TabPFN, predict from a labelled context without updating their parameters and are increasingly used for out-of-distribution (OOD) detection in Web applications, whose test rows come from entities absent from that context. Such applications need open-entity OOD detection: a new entity must not be mistaken for an unknown class, and an unknown class must still be caught. Existing detectors fail in this setting, and an entity-split gap hides the failure: random splits also place the test entities in the context. The failure arises because every TFM score reflects how unfamiliar a row is to the context. This produces an entity-over-class bias: known-class rows of new entities look more OOD than familiar ones, sometimes more than unknown classes. Specifically, the two OOD scores most widely used with in-context TFMs face an entity-class trade-off: the TFM's predictive confidence misses unknown classes, whereas the distance of a test row to the context flags new entities. To address this problem, we hold a group of entities out of the context and score them with the remaining context, which turns them into new entities of known classes and tells us how such rows should score. To measure detection on new entities, we build NEO-Tab (New-Entity OOD detection on Tables), 14 grouped and temporal tables on which all test rows come from new entities. We then propose Context-Calibrated Detection (CCD), which uses these group-out swaps on in-distribution data to calibrate three distances and the confidence against new-entity references. CCD weights them by their sensitivity and sharpness, keeps the model frozen and uses no OOD data. On NEO-Tab, CCD outperforms 16 existing detectors on every metric we report, and its lead holds on three TFM backbones.

cs.IR

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新论文 · v1arXiv:2611.01483

$TF^2$-Steering: Action Refinement with Tactile Forecasts and Feedback via Velocity-Field Steering

Yiming Chen, Xiaofei Wei, Chun Gu, Wei Xiao, Peidong Liu, Li Zhang

原文摘要

Tactile sensing plays a crucial role in everyday human interaction. Consider inserting a plug into a tight socket, vision alone provides limited guidance during the final insertion stage, and we instead rely on both anticipated contact and realized tactile feedback to ajust the insertion motion. The same capability is equally important for robots, particularly in dexterous manipulation tasks. Yet many contemporary robot policies remain vision-dominant. This visual bias, compounded by limited tactile data and sparse contact events, can cause directly incorporated tactile signals to be ignored or even compromise the policy's pretrained capabilities, yielding degrading performance as observed in our experiments. We present $TF^2$-Steering, which refines actions via learned residual steering of the flow-matching action expert's velocity field, using a tactile mixture-of-experts architecture to integrate both tactile forecasts and real-time tactile feedback. Together with specific designed three stages training recipe, $TF^2$-Steering enables tactile signals to correct and, when necessary, redirect the flow-matching action-generation process, while building on the strong action-generation capacity of the vision-based foundation policy. Extensive experiments across six simulated and six real-world manipulation tasks demonstrate the effectiveness of $TF^2$-Steering. It outperforms the strongest evaluated baselines by 10.7 percentage points in average simulation success rate and 9.1 percentage points in average real-world progress success rate. Project page: https://yiming-cc.github.io/tf2-steering/.

cs.RO

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版本更新 · v6arXiv:2410.22931

A Third-Order Gaussian Process Trajectory Representation Framework with Closed-Form Kinematics for Continuous-Time Motion Estimation

Thien-Minh Nguyen, Ziyu Cao, Kailai Li, William Talbot, Tongxing Jin, Shenghai Yuan, Timothy D. Barfoot, Lihua Xie

原文摘要

In this paper, we propose a third-order, i.e., white-noise-on-jerk, Gaussian Process (GP) Trajectory Representation (TR) framework for continuous-time (CT) motion estimation (ME) tasks. Our framework features a unified trajectory representation that encapsulates the kinematic models of both $SO(3)\times\mathbb{R}^3$ and $SE(3)$ pose representations. This encapsulation strategy allows users to use the same implementation of measurement-based factors for either choice of pose representation, which facilitates experimentation and comparison to achieve the best model for the ME task. In addition, unique to our framework, we derive the kinematic models with the closed-form temporal derivatives of the local variable of $SO(3)$ and $SE(3)$, which so far has only been approximated based on the Taylor expansion in the literature. Our experiments show that these kinematic models can improve the estimation accuracy in high-speed scenarios. All analytical Jacobians of the interpolated states with respect to the support states of the trajectory representation, as well as the motion prior factors, are also provided for accelerated Gauss-Newton (GN) optimization. Our experiments demonstrate the efficacy and efficiency of the framework in various motion estimation tasks such as localization, calibration, and odometry, facilitating fast prototyping for ME researchers. We release the source code for the benefit of the community. Our project is available at https://github.com/brytsknguyen/gptr.

cs.RO

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新论文 · v1arXiv:2611.01474

Accounting for Irregular State Transitions through Belief Estimation in Online Medical Consultation

Mingzhe Lu, Xiangru Chen, Jian Qin, Yunpeng Li, Xihan Gao

原文摘要

While online medical consultation broadens access, unprompted patient disclosures and topic shifts frequently disrupt clinician inquiry, leaving clinical states progressively revealed. We characterize these dynamics using both existing evaluation metrics and task-specific interaction metrics designed in this work. Through an empirical study across 16 medical dialogue datasets, we show that authentic online interactions exhibit substantially lower contextual continuity and higher unpredictability than offline and simulated benchmarks. We propose PRISM (Policy Reasoning with Inferred States for Medicine), a framework inspired by partially observable Markov decision processes that decouples latent state estimation from action selection to address drifting dialogue trajectories. Specifically, PRISM factorizes clinical triage into belief estimation, policy optimization, and response realization. By maintaining an evolving posterior belief over consultation states and conditioning action selection on the maximum a posteriori state, PRISM advances clinical goals while accommodating patient-initiated shifts. Extensive evaluations show that PRISM substantially outperforms frontier LLMs and clinical agents, achieving a 22.40 percentage point gain in clinical decision accuracy alongside decisive improvements in response generation, surpassing the strongest baseline by 5.51 points in BERTScore_F1. Furthermore, ablation studies show that providing oracle consultation states consistently boosts response quality across all evaluated baselines by guiding their action reasoning, yielding average gains of 5.58 points in BERTScore_F1.

cs.CL

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版本更新 · v2arXiv:2610.33259

RadonBench: A Benchmark of Classical and Learning-Guided Search Techniques for Problems in Combinatorial Geometry

Xiaoan Xu, Jesus A. De Loera, Ethan X. Fang, Junwei Lu, Luze Xu

原文摘要

Classical optimization and learning-guided search offer complementary approaches to open problems in discrete mathematics, but their outputs are difficult to compare when search spaces, validity checks, and stopping rules differ. We present \emph{RadonBench}, a benchmark for constructing maximum-cardinality Radon-free lattice configurations: finite subsets of $\mathbb{Z}^d$ admitting no integral Radon partition. It remains open whether Radon-free configurations with $11$ points in $\mathbb{Z}^3$ or $21$ points in $\mathbb{Z}^4$ are possible. If they exist, they would improve the corresponding lower bounds on the integer Radon numbers. We compare exhaustive extension, greedy local search, PatternBoost, AlphaZero, and AlphaEvolve. All outputs use one success predicate: a convex target-sized configuration succeeds exactly when its number of integral Radon points is zero. PatternBoost and AlphaZero construct verified Radon-free configurations of sizes $10$ and $20$, one point short of the respective targets; AlphaZero's best convex target terminals have two integral Radon points in both dimensions. Exhaustive extension over fixed seeds and declared finite search boxes likewise reaches a minimum of two integral Radon points. Greedy local search and AlphaEvolve each produce target-sized near misses with one integral Radon point. No audited run reaches zero. These results establish reproducible near-miss baselines over explicitly scoped search domains.

cs.LG

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新论文 · v1arXiv:2611.01473

A Surrogate Model for Variation-Aware Clock-Mesh Analysis and Design-Space Exploration using Graph Neural Networks

Muhammad Hadir Khan, Wajid Ali, Arya Thottiparambil Manikandan, Matthew Guthaus

原文摘要

Clock meshes are widely used in high-performance VLSI systems to minimize skew and tolerate On-Chip Variation (OCV) in nanometer technologies. However, analyzing meshes is difficult because reconvergent paths and multi-source drivers prevent static timing analysis (STA) tools from handling mesh loops, making SPICE the industrial standard. SPICE simulations are accurate but slow, so clock-mesh synthesis relies on approximate models that overlook critical effects such as non-linear buffer driving caused by input slew and input skew. We propose a Graph Neural Network (GNN) based surrogate model of the clock mesh with a new graph representation and a new architecture that predicts power while also improving the delay and slew predictions at the sinks and capturing the skew histogram under OCV. Trained on SPICE data and tested on unseen real designs, our model achieves average errors of $3.40$~ps in delay, $4.14$~ps in slew and $4.46$~ps in skew, compared with $6.28$~ps, $12.35$~ps, and $11.62$~ps for our prior model, and $74.37$~ps delay and $27.51$~ps skew for the approximate method used in current state-of-the-art works. Our model predicts power within $3.4\%$ of SPICE on average, while delivering $18\times$-$1706\times$ speed-ups over the approximate method under OCV modeling, enabling faster and more accurate clock-mesh design-space exploration. A case study on a real design further shows that our surrogate model finds buffering candidates that, at a balanced tradeoff, improves $99^{\text{th}}$-percentile skew by $49.1\%$, reduces power by $25.7\%$, and reduces area by $37.3\%$ at the cost of a $6.8\%$ increase in $99^{\text{th}}$-percentile maximum slew as compared to the state-of-the-art work.

cs.AR · cs.LG

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新论文 · v1arXiv:2611.01472

Causal Optimal-Transport Attention: Positional Priors and Length Extrapolation

Ming Lian

原文摘要

Causal masking obstructs the column balancing used by optimal-transport (OT) attention. We study a prefix-consistent unbalanced operator and characterize the positional prior it induces. Under bounded scores, for any fixed balancing strength $ρ<1$, its fixed point dilutes attention: every fixed window receives a vanishing share as the context grows. For constant scores, we derive an exact scale-invariant prior. We then classify positional encodings by whether their distant attention mass remains bounded away from one. Discrete window-mass bounds quantify the finite-length dilution of summable log-distance priors near exponent one. Preregistered experiments with 25M- and 100M-parameter language models establish improved loss extrapolation over softmax; exploratory comparisons favor an explicit distance bias at a fraction of the cost. CABLE outperforms RoPE with evaluation-time YaRN on every seed at three training lengths. Changing only the bias beyond the training length moves long-context loss in the predicted direction on all 30 tested checkpoints. Even with exponents constrained above one, many heads approach the summability boundary during training. A margin selected by prior-only calculations for a specified window, length range and growth tolerance controls distant-mass growth in the tested setting. These results connect loss extrapolation to positional decay over the lengths actually evaluated.

cs.LG

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新论文 · v1arXiv:2611.01467

See, Think, Use: Diagnosing Perception and Reasoning Failures in VLMs

Jiacheng Ding, Yanliang Qi, Cong Guo, Peihua Mai

原文摘要

We present See, Think, Use, a protocol that traces each answer of a vision-language model to perception, reasoning or the use of perception. The diagnosis shows a developer which stage to improve and directs test-time compute to the stage that fails. Benchmarks score the final answer, and an answer that rests on the image earns the same credit as a guess or a memorized prior. The protocol asks the model two extra questions per item and needs no access to its weights. A localization probe asks for a box around every instance and measures seeing, and a text-replacement probe answers from a description and measures thinking. Use is the rate at which the answer follows the model's own perception across counterfactual image pairs. The rate needs no answer labels, because the question and its prior are the same on both images. On two model families and six datasets the protocol shows that the knob that helps follows the task family. Answers follow the model's perception except on images that contradict a common prior, and asking the model to localize first repairs this failure when its boxes are right. Allocating compute by the measured stages comes close to the best uniform policy at lower cost and beats a random allocation of the same compute wherever a knob raises the short stage. We will release the code, the prompts and the per-question outputs.

cs.CV · cs.AI · cs.CL

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