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新论文 · v1arXiv:2611.08652

An Anatomy-guided 3D Segmentation Pipeline for Prostate Surgical Phantoms from CT Scans

Iven Chen, Kuan-I Chung, Ayberk Acar, Susheela Sharma Stern, Alejandro O. Chara, Jie Ying Wu, Daniel Moyer

原文摘要

Full autonomy has been a long-term goal in robotic surgery and a near-to-medium-term goal for prostatectomy. Due to the relatively closed structure of the procedure, its standard-of-care robotic/laparoscopic solutions, and procedure economics, prostate procedures are an optimal early target for surgical automation. Prototyping and testing of possible solutions is done in constructed laboratory phantoms, either synthetic tissues or animal phantoms. To enable measurement of perceptual and robotic motion error, phantoms undergo volumetric imaging; these images are critical to both a potential deployed automatic system, used in planning motion and treatment (cutting, ablation, suturing, etc.), as well as post-hoc assessment of system performance. Rapid iteration on these phantom models yields small, inconsistent datasets, while the current workflow for human annotation is often anatomically unreliable and prohibitively time-consuming, rendering deep learning unsuitable at reasonable scales. To address these limitations, we present an automated 3D segmentation pipeline that uses classical medical image-processing techniques to encode the known anatomy of the phantoms as explicit geometric and intensity priors, which requires no training corpus and is transferred to each new phantom revision without re-annotation. Across 57 scans spanning multiple phantom revisions, our pipeline recovers the capsule interior at 0.83 median Dice on 11 contrast-suppressed scans and detects all 32 annotated focal lesions without false positives, completing a full scan in 2-8 minutes on commodity CPU hardware versus 1-2 hours by hand.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08651

Turkish Multimodal Video Fact-Checking: A New Dataset and a Comparison of Zero-Shot Agentic and Fine-Tuned Systems

Azra Açıl

原文摘要

Video-based misinformation spreads rapidly on social media, but almost all existing fact-checking systems focus exclusively on English text. Tools for low-resource languages such as Turkish are severely limited. In this work, we present TeViD (Turkish Video Fact-checking Dataset), to the best of our knowledge the first multimodal video fact-checking dataset for Turkish. We compiled the dataset from articles published by Teyit.org, an IFCN-certified organisation, and enriched it with translated Snopes articles and synthetic examples to reduce class imbalance. Building on TeViD, we develop two verification pipelines that share the same open-source base model. First, in 3MFact-TR (Pipeline 1), we adapt the multi-role 3MFact framework to Turkish with the Qwen2.5-7B large language model and the Qwen2-VL-7B vision-language model, and add three modules of our own: a semantic inconsistency checker, a 5W1H question manager, and a bilingual evidence retriever that excludes fact-checking sites to prevent leakage. Second, as our Fine-Tuned Model (Pipeline 2), we fine-tune a single Qwen2.5-7B with Low-Rank Adaptation (LoRA) to produce closed-book verdicts from the claim and the video's textual description. We report accuracy, macro F1, True class F1, and True class recall for both systems, together with ablation studies.

cs.CV · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08650

Differentially Private All-Pairs Min Cut Values

Anders Aamand, Justin Y. Chen, Mina Dalirrooyfard, Slobodan Mitrović, Sandeep Silwal, Yinzhan Xu

原文摘要

We study the problem of privately releasing the min cut values of all pairs of vertices in a given $n$-node weighted graph, under the edge-neighboring model. Straightforward application of advanced composition yields an approximate DP algorithm that outputs all min cut values up to $\tilde{O}(n)$ additive error. Prior works [Dalirrooyfard, Mitrovi{ć} and Nevmyvaka, 2023; Aamand et al., 2026] have established that $\tildeΘ(n)$ is the correct bound if the algorithm also needs to output cuts that realize the approximate min cut values. On the other hand, simply outputting the values could be an easier problem, but prior algorithms have been stuck on the $\tilde{O}(n)$ bound. In this work, we give an approximate DP algorithm outputting all-pair min cut values with $\tilde{O}(\sqrt{n})$ additive error, beating the linear error barrier. Our algorithm applies the sparse vector technique [Dwork and Roth, 2014] to maintain a hierarchical decomposition of the vertices based on the min cut structure. We also prove an $Ω(\log^{3/2} n)$ lower bound for this problem.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08649

VoluCompose: Constructing Compact Volumetric Visual Representations for Medical Image Understanding

Zanting Ye, Yunrui Lin, Shengyuan Liu, Jing Hao, Chen Hu, Yefeng Zheng, Lijun Lu

原文摘要

Medical vision--language models can interpret volumetric images, but processing full volumes is costly because hundreds of slices produce long, redundant visual-token sequences. Existing compression methods often couple content selection with budget determination. We observe that neighboring slices share visual features and that attention concentration varies across visual encoders. We propose VoluCompose, a training-free, model-agnostic framework that separates what visual content to retain from how many tokens to allocate. Its hyperbolic token composition module groups contiguous slices into ordered support units and uses the Lorentz distance to account for feature magnitude and direction during token allocation, anchor selection, and refinement. Its capacity control module uses visual-encoder attention entropy to allocate more tokens when attention is more dispersed. We evaluate VoluCompose on two tasks across three datasets and four backbones. Across nine calibrated settings, VoluCompose attains the highest mean quality--retention hypervolume among the compared methods. It achieves a mean relative quality of 121.68\% against the uncompressed references while retaining 22.78% of visual tokens on average. Compression remains robust even at a 10% token budget. Across settings, the lowest ROUGE-L ratio to the uncompressed reference is 96.4\% for VoluCompose. The corresponding minima for the compression baselines range from 15.0% to 70.5%. Code is available at https://github.com/yezanting/VoluCompose.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08645

From Geometry to Function: A Multi-Task LiDAR Benchmark Dataset for Semantic and Functional Parsing of Urban Facades

Abdiaziz Muse, Tooba Javed, Tao Pu, Jiju Poovvancheri, Dong Chen

原文摘要

Understanding building facades is central to urban digital twins, heritage modelling, and sustainable city analysis, yet existing LiDAR benchmarks stop at geometry and semantic segmentation and offer little support for reasoning about the functional role of each architectural element. We introduce HFX3D, a large-scale urban facade benchmark of approximately 492 million LiDAR points from 26 buildings in Halifax, Canada, captured across four seasons, day and night, and varied weather. Each facade is annotated at three tiers, semantic, instance, and per-instance functional attributes, making it, to our knowledge, the first facade dataset to pair geometry and semantics with functional labels. Annotations are produced by a semi-automatic pipeline that combines geometry-guided heuristics with expert validation. We benchmark representative point-based, graph-based, convolutional, and transformer architectures and find that semantic segmentation is already challenging under severe class imbalance, while instance separation of repetitive structures and functional reasoning from geometry are substantially harder. The benchmark establishes a new foundation for function-aware 3D urban understanding. Project page: https://jijup.github.io/hfx3d-benchmark/ ; code: https://github.com/jijup/hfx3d-benchmark

cs.CV · cs.AI · cs.GR · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08643

FinEvolveKG: Controlled and Attributable Prompt Evolution for Financial Knowledge Graph Construction

Abhinav Arun, Jayita Bhattacharyya, Fabrizio Dimino, Bhaskarjit Sarmah, Stefano Pasquali

原文摘要

Financial institutions construct knowledge graphs (KGs) from shared SEC 10-Ks to serve risk analysis, portfolio management, and investment research, whose analytical needs differ. Extraction must adapt without fragmenting shared grounding, typing, and temporal rules. Hand-authored persona prompts and fine-tuning add cost. Weight-free prompt optimization offers an alternative, but unconstrained evolution of a monolithic prompt can rewrite shared rules alongside persona guidance, obscuring changes and complicating review. We introduce FinEvolveKG, a KG-construction harness separating reusable general and persona-policy blocks and mechanically confining GEPA, a reflective prompt optimizer, to the designated block. During persona adaptation, the approved general block remains byte-stable and every optimizer edit is localized to a separately versioned persona policy. A dual objective balances generic temporal-KG quality with persona-relevant facts, motifs, and multi-hop connectivity. We evaluate five mutation scopes on ticker-disjoint 10-K splits, with three seeds for the persona-conditioned arms (v3-v5) across three personas. On the three-seed mean, the complete two-stage block-constrained workflow (v4) improves over the frozen prompt baseline (v1) by 19.4-30.9% and over the tested unconstrained full-prompt workflow (v5) by 2.2-6.4%, leading v5 on both objective components and winning all nine paired-seed comparisons with the lowest seed variance in every persona. Without further optimization, v4 also exceeds v5 across all evaluated out-of-distribution (OOD) shifts. An SME audit supports using the generated triplets as ground truth; a blinded two-SME audit favors v4 over v5 in 70.1% of contrast-enriched judgments. These results show that mechanically attributable evolution can coexist with strong empirical performance, without attributing the gains causally to the boundary.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2604.27266

AutoREC: A reinforcement learning platform for equivalent circuit model generation

Ali Jaberi, Yonatan Kurniawan, Robert Black, Shayan Mousavi M., Kabir Verma, Zoya Sadighi, Santiago Miret, Jason Hattrick-Simpers

原文摘要

This paper introduces AutoREC, an open-source Python platform for developing, training and evaluating reinforcement learning (RL) agents that automatically generate equivalent circuit models (ECMs) from electrochemical impedance spectroscopy (EIS) data. Although ECMs are widely used to interpret EIS measurements, their identification typically relies on manual trial-and-error, requiring domain expertise and limiting scalability, particularly in autonomous experimental pipelines such as self-driving laboratories. In AutoREC, ECM generation is formulated as a Markov decision process in which an RL agent sequentially modifies a circuit topology based on the current state, available actions and feedback from the resulting model. The platform supports an end-to-end workflow encompassing EIS preprocessing with selectable impedance representations, agent setup and training, ECM generation for new measurements and visualization-based evaluation and analysis of agent decision-making. AutoREC implements a configurable Double Deep Q-Network agent with prioritized experience replay and a dedicated deadloop mitigation strategy for navigating the complex circuit-generation action space efficiently. To demonstrate the platform, we trained a representative agent on 1350 synthetic spectra from five reference circuit families, evaluated it on 150 held-out synthetic spectra and applied it to 32 previously unseen experimental spectra from battery, corrosion, oxygen evolution reaction and CO2 reduction systems. These case studies illustrate the end-to-end capabilities of AutoREC while revealing challenges associated with experimental complexity and limited training-data coverage. The demonstrated agent serves as a reference implementation; AutoREC provides an extensible foundation through which users can develop and evaluate agents tailored to their specific electrochemical systems and research objectives.

cs.LG · cond-mat.mtrl-sci

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.03100

LUCA: A Synthetic World for Experimental Reasoning

Anna Gogleva, Thomas Charman, Martin Balla, Karl Gemayel

原文摘要

An experimental agent can succeed for the wrong reasons. Evaluations, however, often focus on the outcome, not the experiments, evidence, and inferences that produced it. We introduce LUCA, a configurable synthetic world for evaluating this process under fully known ground truth. Agents learn unknown regulatory systems through budgeted experimentation, then predict unseen interventions, diagnose hidden faults, and attempt rescues. This setup allows us to evaluate experiment selection, prediction, diagnosis, and intervention separately. Across seven frontier LLMs and three baselines, these capabilities dissociate sharply. Agents frequently rescue malfunctioning systems while diagnosing the wrong fault; diagnosis varies 7x across models while prediction varies only 1.4x. Models also adopt surprisingly different experimental strategies, and greater exploration does not reliably yield better performance. In one failure mode, agents often perform the experiment needed to distinguish competing causes, but still fail to use the resulting evidence. LUCA turns these hidden failures into measurable behaviours, providing a controlled environment to study where experimental agents succeed and fail. Code: https://github.com/RelationRx/luca.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08640

Aspect-wise Pairwise Tournaments for Test-Time Selection

Hannah Guan, Yilun Du

原文摘要

Scaling test-time compute has proven highly effective at improving LLM reasoning. A popular method is best-of-n sampling, where a model generates many candidate solutions and one is selected. This motivates the question: how can we select the best candidate? When humans make choices, we find it more natural to compare two options than judge each in isolation. Additionally, when the options become complex, we like to judge the options one aspect at a time, such as comparing two job candidates on experience, communication, etc. In the test-time setting, candidate solutions should be judged in the same way. Here we introduce Aspect-wise Pairwise Tournaments (APT), which verifies by comparison and compares by aspect. Each matchup of a single elimination tournament is decided by a vote over multiple off-the-shelf aspect verifiers, each expressing a pairwise preference along its own aspect. Across five domains and three generator LLMs, APT improves over binary multi-aspect approval, self-consistency and reward model verification, with the largest gains on open-ended tasks. We find that both components matter; neither binary approval over the same aspects nor a generic pairwise judge at a matched budget matches APT. Because aspects are specified in natural language, APT also lets users steer selection toward properties they care about, better aligning with user preferences without needing to regenerate solutions. No component is trained, so APT transfers to new domains by simply rewriting prompts.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08638

MORTIS: Compiler-Guided KV-Cache Management for LLM Agent Programs

Xiaoyu Ma, Jayanaka L. Dantanarayana, Savini Kashmira, Baichuan Li, Lingjia Tang, Jason Mars

原文摘要

Existing agent serving systems divide each prompt into a reusable prefix and an opaque suffix, managing the KV-cache at this coarse granularity without distinguishing the components within each part. We observe that prompt components differ in which LLM calls share their values and how long those values remain reusable, a property we term intra-prompt heterogeneity. Our key insight is that, in frameworks that generate prompts from typed LLM function calls, these components correspond to program variables, allowing compiler analysis to recover their sharing patterns and lifetimes. We present MORTIS, a compiler-serving co-design that combines this analysis with runtime observations to improve KV-cache management. MORTIS reorders prompt components to expose additional prefix reuse and uses their predicted reuse and lifetimes to guide cache retention, eviction, and prefetching. Our evaluation shows that MORTIS achieves up to 1.99x job completion time (JCT) speedup and 1.97x throughput improvement over SGLang, with geometric-mean speedups of 1.45x and 1.49x, respectively.

cs.OS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08637

Branch-Extended Automatic Differentiation

Jan Hückelheim, Jeffrey Larson, Matt Menickelly

原文摘要

We present branch-extended automatic differentiation (BEAD), a software package that provides information about inactive branches of piecewise-differentiable functions, intended for use in nonsmooth optimization algorithms. BEAD is implemented in Python, building on the popular JAX framework. For supported nonsmooth operations such as abs and max, BEAD identifies alternative branches within tolerance from the given input, and computes gradients of the resulting branches extended to the given input. Such information has been previously used for robust optimization methods, but has required error-prone and tedious manual differentiation, unlike BEAD, which is fully automated for functions written in NumPy-like JAX code. Our experiments show that our approach is as accurate as manually written derivative code, and offers more flexibility in modifying the function and choosing between branch-discovery rules. While we were originally motivated by the manifold sampling algorithm for nonsmooth composite objectives, we also integrate BEAD branch-extension gradients into a nonsmooth BFGS-SQP solver. On two small nonsmooth test problems, the resulting path-aware search resolves failures or substantially reduces the number of evaluations required by the BFGS-SQP solver.

math.OC · cs.MS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.08711

Deep Learning-Based Tracking and Lineage Reconstruction of Ligament Breakup

Vrushank Ahire, Vivek Kurumanghat, Mudasir Ganaie, Lipika Kabiraj

原文摘要

The disintegration of liquid sheets into ligaments and droplets involves highly transient, multi-scale dynamics that are complex to quantify from high-speed shadowgraphy images. Identification of droplets, ligaments, and blobs formed by the breakup of a liquid sheet, and their tracking from frame to frame, is an essential part of spray analysis. Analysis of such processes poses a fundamental challenge for conventional multi-object tracking frameworks, which enforce strict one-to-one temporal associations and cannot represent one-to-many fragmentation events. We present a two-stage deep learning framework that detects droplets and ligaments and identifies their temporal relationships across successive frames, modeling ligament deformation, fragmentation, and parent-child lineage. In the first stage, a Faster R-CNN detector with a ResNet-50 backbone and Feature Pyramid Network classifies ligaments and droplets in recordings of two impinging Carbopol gel jets. A morphology-preserving synthetic data generation strategy enriches the training set without physically implausible configurations, achieving a held-out test F1 score of up to 0.872 across fourteen original-to-synthetic data configurations, with robustness tested on different atomization conditions and liquid compositions. In the second stage, a Transformer-augmented multilayer perceptron classifies inter-frame object associations into continuation, fragmentation (one-to-many), and non-association using physics-informed geometric features. Despite severe class imbalance, the model achieves 86.1% accuracy, 93.2% precision, and perfect recall (1.00) for fragmentation events on the held-out test set. Together, the two stages preserve parent-child lineage and enable extraction of breakup statistics, including fragment multiplicity and droplet size distributions, offering an automated analysis of the primary mode of atomization.

cs.CV · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08634

Benchmarking Machine Learning for Chaotic Three-Body Dynamics in Astrophysics

Sheng Wan, Pavan Vynatheya, Duo Xu, Li Kevin Wenliang, Ye Zhu

原文摘要

Machine learning (ML) and generative AI have recently made impressive progress in understanding physics. We explore a hierarchy of prediction problems stemming from chaotic three-body systems and benchmark existing ML approaches, including general-purpose prediction algorithms and those that build on top of foundational physics models. We first simulate the trajectories of triple systems near the critical boundary of stable/unstable initial conditions. We then design a hierarchy of learning tasks, focusing mainly on two broad problems: stability prediction and trajectory prediction. The former classifies whether a given triple system has diverged or not up to a specific horizon, while the latter predicts the exact trajectory, both given suitable input features. We benchmark a range of ML approaches, from simple regression methods to state-of-the-art foundational physics models, across these tasks, and compare them against astrophysical methods such as the Mardling-Aarseth stability criterion. Our experiments reveal a clear order of task difficulties in chaotic three-body dynamics: early dynamics enable strong stability prediction, while precise long-horizon trajectory forecasting remains difficult. Code and dataset are publicly available at https://github.com/Simonwan03/ThreeBodyMLBench.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.02050

A Memory Efficient Unified Algorithm for Online Learning of Linear Dynamical Systems

Yuval Ran-Milo, Angelos Assos, Elad Hazan

原文摘要

Motivated by the challenge of stabilizing a general unknown linear dynamical system (LDS) from observations, we study the natural prerequisite of online prediction. Our goal is to achieve sublinear regret with a memory footprint that adapts to the intrinsic complexity of the dynamics rather than the full hidden-state dimension. We focus on the practically central regime of systems with low instability complexity -- eigenvalues outside the real stable interval that do not decay rapidly, together with non-semisimple modes -- potentially embedded in an otherwise stable real spectrum of much higher dimension; we write $k$ for this count. This regime is the primary setting in which stabilization is plausible: we show that many systems with high instability complexity cannot be stabilized without exponentially large controls. Thus, prediction is meaningful for stabilization precisely when the instability complexity is small. Within this regime, we introduce a unified online algorithm that handles every LDS (including non-diagonalizable systems with complex or exploding modes) with a learnable parameter count of $\widetilde{O}(k)$. Finally, we prove a lower bound showing that $k$ is a valid complexity measure: any filter-based predictor needs at least $k$ filters. Experiments corroborate our theory: on a high-dimensional system, our predictor sharply outperforms prior methods at an equal parameter budget.

cs.LG · eess.SY

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2608.05466

Recursive Synthesis for Long-Horizon Terminal Tasks

Zhongzhi Li, Yucheng Shi, Zongxia Li, Ruhan Wang, Anhao Li, Jiaxi Li, Junyao Yang, Lei Ke 等 14 位作者

原文摘要

High-quality long-horizon training data for terminal agents is costly and difficult to scale, as manual authoring is expensive and direct LLM generation often produces inconsistent instructions, environments, solutions, and verifiers. We present Recursive Synthetic Terminal Tasks (RST), a recursive verified synthesis framework for constructing long-horizon terminal-agent tasks at scale. Starting from verified seed tasks, RST extends the reference solution, realigns the verifier and instruction to the new workflow, validates the result in a fresh sandbox, and reuses accepted tasks as seeds for subsequent rounds. Across fifteen recursive rounds, RST produces 37,484 synthesized terminal-agent tasks at roughly $0.05 per task. Task difficulty increases substantially over rounds: the median reference solution grows from 67 to 374 lines, the median number of executed commands grows from 40 to 244, and DeepSeek-V4-Pro pass@4 drops from 90% at R1 to 2.5% at R15. To demonstrate training utility, we collect rejection-sampled Qwen3.5 trajectories on the synthesized tasks and use them for supervised fine-tuning. Fine-tuning on these trajectories improves Qwen3.5-27B and Qwen3.5-122B-A10B by up to 10 points on Terminal-Bench 2, Terminal-Bench Hard, and Long-Horizon Terminal Bench, while agentic PPO lifts Qwen3.5-27B to 49.44%, 32.00%, and 22.07% on the three benchmarks, corresponding to relative gains of 20.0%, 41.2%, and 21.9% over the base model. Moreover, after 15 rounds, the recursion shows no ceiling: synthesis yield and validation rates remain stable as difficulty keeps climbing, indicating that the process can continue well beyond the scale reported here.

cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10040

SPRING-5: Measuring Tool Documentation Repair for LLM Agents by Efficiency

Guan Zheng Huang

原文摘要

Methods that repair tool documentation automatically are mostly evaluated on task accuracy. We report a benchmark that scores them on cost. SPRING-5 is 20 tool families and 344 tasks, each with a deliberately degraded specification, a hand-written honest one, a deterministic backend and an answer key, scored on calls charged against the ideal call path. A wrong answer is charged the full call budget, so this cost index largely tracks the completion rate, and we report calls per correct task beside it, which carries no such charge. Over 2,644 baseline sessions, 54% of wrong answers arrive with no rejected call, no refusal and no retry. The benchmark carries three validity instruments. A build-time ceiling assertion requires the honest specification to reach the ideal path and fails systematically on only one family. A dud rule names the families a stronger model solves cold and catches four. A directly measured noise floor shows that two evaluations of a byte-identical specification differ by a 95% factor of 0.64 to 1.56, so about one comparison in eight crosses a conventional win threshold on noise alone, and the cheap way of estimating that floor is biased by which families happen to go unedited. Against these instruments, a schema-only method closes 1% of the baseline-to-ideal cost gap, an online self-evolution loop 13%, one batch rewrite over every training transcript 33%, and a variant given correctness labels 58%. The batch advantage over the loop is the evidence it reads: held to the loop's exposure it is no longer detectable. With five and ten tools in context the effect survives, the baseline cost does not change, and only machine-written descriptions sent calls to the wrong tool. On BFCL, 62% of held-out queries are already answered from the shipped documentation, which explains a pooled null there. Code, data and runs are released.

cs.SE · cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.04039

Safety and accuracy diverge across deployment conditions in clinical large language models

Sebastian Wind, Tri-Thien Nguyen, Jeta Sopa, Mahshad Lotfinia, Sebastian Bickelhaup, Michael Uder, Harald Köstler, Gerhard Wellein 等 12 位作者

原文摘要

Clinical large language models (LLMs) are commonly scaled by increasing model size, context length, retrieval complexity, or inference-time compute, on the assumption that higher accuracy implies safer behavior. In medicine, where a few confident or evidence-contradicting errors can outweigh average performance, this assumption may be incomplete. We introduce Safety-Focused Evaluation of Scaling (SaFE-Scale) and instantiate it on RadSaFE-200, a 200-question text-based radiology multiple-choice benchmark with clinician-defined clean evidence, conflict evidence, and option-level labels for high-risk error, unsafe answer, and evidence contradiction. Thirty-four local LLMs were evaluated across six deployment conditions spanning closed-book, evidence-grounded, standard and agentic retrieval- augmented generation (RAG), and max-context prompting. Clean clinician-written evidence, used as an upper-bound, produced the strongest joint gain, raising mean accuracy from 73.5% to 94.1% and reducing high-risk error from 12.0% to 2.6% and dangerous overconfidence from 8.0% to 1.6%. Standard and agentic RAG, max-context prompting, and inference-time compute improved selected metrics but did not reproduce this safety profile. Clinically, consequential errors concentrated in a small subset of questions, and variance decomposition placed deployment condition above model family on every safety metric. Within this evaluation, clinical LLM safety did not track accuracy scaling but emerged as a deployment-level property.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.06630

The Sparsity Whisperer

Linghao Kong, Inimai Subramanian, Micah Adler, Dan Alistarh, Dan Gutfreund, Nir Shavit

原文摘要

Pruning reduces the inference cost of large language models, but existing criteria primarily preserve large activations or reconstruct layer outputs. We argue that this overlooks a key computation performed by particularly sparsity-sensitive neurons in the MLP up and gate projections: separating similar inputs into dissimilar outputs. This suggests that effective pruning should preserve not only activations, but also the differences between outputs more broadly. We introduce a family of difference-informed pruning methods built upon this principle. Wisp+ is a neuronwise, update-free method that prioritizes the input pairs each neuron separates most strongly relative to their distance. Wisp provides a more efficient layerwise alternative that scores weights using shared input-difference norms. Finally, Whisper extends global difference preservation to second-order reconstruction using a lightly regularized difference Hessian. Across Llama 2 and 3.1 models from 7B to 405B parameters, our second-order variant consistently improves over strong reconstruction-based baselines, while our update-free variants improve over activation-aware baselines, especially in constrained settings. The improvements over Wanda and SparseGPT extend to structured sparsity, downstream evaluations, and other model families. Augmenting stronger techniques such as RIA and ALPS with our difference-informed criteria yields further improvements, shifting the overall accuracy-runtime frontier outward at negligible additional cost. These results suggest that preserving output differences is a broadly useful and composable signal for post-training LLM sparsification.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.23509

Internal Safety Collapse in LLMs and Coding Agents

Yutao Wu, Xiao Liu, Hanxun Huang, Xiang Zheng, Yige Li, Yifeng Gao, Cong Wang, Bo Li 等 10 位作者

原文摘要

LLMs and coding agents can now tackle complex, long-horizon tasks through advances in reinforcement learning, but stronger task-solving capabilities do not necessarily preserve safety alignment. Recent AI agent incidents involving reward hacking indicate how optimizing for task success can bring learned capabilities into conflict with safety alignment. In this work, we identify a critical failure mode that manifests this conflict, which we term Internal Safety Collapse (ISC). When completing otherwise benign coding tasks, AI agents can prioritize satisfying program requirements over safety constraints, generating large volumes of sensitive content, including unsafe outputs, as task data. To study this phenomenon, we introduce TVD (Task, Validator, Data), a framework for building controlled workflows that require sensitive content for completion. Through extensive experiments, we observe instances of highly harmful content that closely resemble outputs from early, unaligned LLMs in 2023. We further find that ISC can be effectively triggered even in highly capable LLMs such as Claude Fable 5. These results indicate that, despite substantial alignment efforts, frontier LLMs remain internally unsafe: safety alignment primarily reshapes surface behavior without eliminating the underlying knowledge that enables unsafe outputs. Our findings underscore the need for caution when deploying LLMs in high-stakes settings, such as scientific pipelines and autonomous agents. Content Warning: This paper contains examples of harmful content. Source code: https://github.com/wuyoscar/Internal-Safety-Collapse

cs.CL · cs.AI · cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.00405

Behavior Graphs: Unsupervised Graph Structures for Interpreting Robot Deployment Data

Erik Bauer, Jakob Thumm, Christopher Agia, Rohan Sinha, Chen Xu, Haruki Nishimura, Jeannette Bohg, Marco Pavone 等 9 位作者

原文摘要

Data flywheels promise continuous improvement via insights from deployment data. Understanding how the deployed model succeeds and fails can steer subsequent data collection towards patching failures and reinforcing successes. However, robot deployments create large volumes of high-dimensional rollout data, which is challenging to understand and confidently ground data collection decisions in. To make deployment data easier to analyze and act on, we propose behavior graphs, which compress policy rollouts into a single interpretable, directed graph. We construct the graphs by learning to identify similar behavior modes across rollouts, connecting the behaviors temporally, and finally linking them to outcomes. In a user study on three real-world tasks, where participants analyzed rollouts to choose a data collection strategy, 16 of 18 preferred behavior graphs to rollout videos, reaching comparable conclusions with 40.3% lower perceived temporal demand and 37.6% lower effort. Beyond interpretability for deployment data, behavior graphs are capable progress predictors: across 43 datasets, they improve the rank correlation between predicted progress and true temporal order by 22.9% and reduce mean absolute error by 53.5% relative to the strongest baseline. As the number of deployed robots grows, behavior graphs lend structure to high-dimensional policy rollouts and make deployment data easier to interpret and reason about.

cs.RO · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2602.13739

XIT: Exploration and Exploitation Informed Trees for Active Gas Distribution Mapping in Unknown Environments

Mal Fazliu, Matthew Coombes, Sen Wang, Cunjia Liu

原文摘要

Mobile robotic gas distribution mapping (GDM) provides critical situational awareness during emergency responses to hazardous gas releases. However, most systems still rely on teleoperation, limiting scalability and response speed. Autonomous active GDM is challenging in unknown and cluttered environments, because the robot must simultaneously explore traversable space, map the environment, and infer the gas distribution belief from sparse chemical measurements. We address this by formulating active GDM as a next-best-trajectory informative path planning (IPP) problem and develop XIT (Exploration and Exploitation Informed Trees), a sampling-based planner that grows a single shared tree toward multiple frontier goals, coupling exploration of unresolved regions with informative gas sampling en route. XIT draws a batch of samples from an Upper Confidence Bound (UCB) information field derived from the current gas posterior and expands the tree using a cost that trades off travel effort against information acquisition. To enable plume-aware exploration, we introduce the gas frontier concept, defined as unobserved regions adjacent to high gas concentrations, and propose the Wavefront Gas Frontier Detection (WGFD) algorithm for their identification. High-fidelity simulations demonstrate the benefits of XIT in terms of GDM quality and efficiency, and a scaled real-world experiment demonstrates its practicality. Although developed for active GDM, XIT is applicable to other robotic information-gathering tasks in unknown environments that face the exploration and exploitation trade-off.

cs.RO · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.31780

Panoptic Scene Program Diffusion Transformer

Chika Maduabuchi

原文摘要

Modern text-to-image models produce high-fidelity images but still struggle with compositional prompts that require instance identity, attribute ownership, counting, spatial ordering, and role-sensitive relations. We introduce Panoptic Scene Program Diffusion Transformer (PSP-DiT), a diffusion-transformer architecture that treats a panoptic scene program as a first-class latent variable rather than an external control signal or post-hoc parse. PSP-DiT jointly denoises image latents and scene-program latents through coupled transformer streams, while panoptic grounding and cycle-consistency objectives tie object instances, attributes, relations, and counts to visual support in the generated image. Under matched training and inference settings, PSP-DiT improves over a strong flat-text baseline across GenEval 2, SANEval-Simple, PSG-Score, and DetailMaster, with the largest gains on counting, attribute binding, role-sensitive relations, and long structured prompts. The method preserves image quality, adds modest inference overhead, and remains robust to imperfect scene programs.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08615

s$^3$: Simple Scaling of Swarms via Cooperative Multi-agent RL

Kunal Jha, Gonçalo Faria, Shuyue Stella Li, Scott Geng, Faeze Brahman, Noah A. Smith, Yulia Tsvetkov, Max Kleiman-Weiner 等 9 位作者

原文摘要

Groups can achieve more than individuals in the same amount of time, but only if their members coordinate: they must decide who works on what and what to share. However, this organization in multi-agent systems today usually comes from arbitrary, static harnesses designed a priori without adapting to agent behaviors or task specifications. We ask whether agents can instead learn to organize themselves. We introduce s$^3$, the first multi-agent reinforcement learning method in which agents decide for themselves how long to work alone and when to share findings or read teammates' messages, with no predefined organization or roles. Our s$^3$ agents learn to take different approaches, read each other's work, exhibit emergent roles that complement each other in the swarm, and generalize to bigger teams. We study both coordination, where agents must combine private information, and parallel exploration, where sharing discoveries can make search more efficient. With matched training and inference budgets, s$^3$, swarms evaluated with twice as many agents as in training solve over 25$\times$ more coordination problems than the strongest baseline, and in exploration, communicating agents reduce repeated work by nearly 50% and outperform best-of-N by up to 9.4 percentage points. Rather than being designed in advance, s$^3$ enables organization to emerge from a shared goal. This opens the door to capable systems built from many models that learn to work together.

cs.MA · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.04678

Lower Bounds for Adaptive Relaxed Locally Decodable Codes via the Capped Horvitz--Thompson Estimator

Guy Goldberg, Tom Gur, Sidhant Saraogi

原文摘要

We prove a nearly optimal lower bound on the blocklength of general relaxed locally decodable codes (RLDCs), extending the result of [GGS26] to nonlinear encodings and adaptive decoders with two-sided error. Namely, we show that any $q$-query RLDC $C\colon \{0,1\}^k \to \{0,1\}^n$ must satisfy $n = k^{1+Ω(1/q)}$, closely matching the upper bound of $n = k^{1+O(1/q)}$ by Ben-Sasson, Goldreich, Harsha, Sudan, and Vadhan (STOC 2004). Our proof reduces showing RLDC lower bounds to estimating the output distribution of adaptive decoders. To this end, we introduce a capped Horvitz--Thompson estimator with uniformly bounded discrete derivatives on the $p$-biased cube. We establish its concentration of measure via self-bounding inequalities and control the downward bias induced by capping through a spreadness argument.

cs.CC · cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08610

InstaGen: Group-Shared Importance Sampling for Fast Video Generation

Kislay Aditya Oj, Kumar Krishna Agrawal, Sri Surya Varma Datla, Aditya Desai

原文摘要

Diffusion Transformers (DiTs) are the dominant architecture for video generation, but their quadratic attention complexity results in substantial computational cost and latency. Permuted block-sparse attention (PBSA) offers a promising approach to reducing this cost by selectively computing blocks of a semantically permuted attention matrix. However, existing approaches typically select only the top few key blocks for each query block, following a top-$p$ paradigm. We find that top-$p$ selection can be highly sample inefficient in high-entropy attention regimes, which are common in video generation. In these regimes, we show that sampling can provide substantially better approximations under the same computational budget. More generally, we cast block selection as importance sampling, where the top-scoring keys form an exact set that is computed in full and the remaining keys are represented by a sample drawn in proportion to their estimated attention and reweighted by inverse sampling probability. Building on this formulation, we present InstaGen, which can be parameterized by any PBSA method: it replaces the method's block selection with an exact set plus a weighted sample. Each query cluster draws one sample shared by all its queries, so the sampled keys form blocks amenable to efficient GPU execution. In this paper, we take the SVG family of PBSA methods and show that, on Wan2.1-T2V-1.3B and Wan2.1-T2V-14B, InstaGen parameterized by SVG2 improves PSNR over SVG2 by 3.4 dB and 2 dB, respectively, at matched attention density, and by 1.6 dB and 2.2 dB at matched wall-clock time. On Wan2.1-T2V-14B, InstaGen also beats SVG-EAR by 1.1 dB PSNR at nearly the same density while running faster (1.65x against 1.48x over dense).

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08486

生成式AI时代下，我们是否怀念那些精心制作图表的夜晚？对Teaser图与框架图的再思考

Do You Miss the Nights We Spent Crafting Figures? Rethinking Teaser and Framework Figures in the Age of Generative AI

Xiaoqi Zhao, Youwei Pang, Kailai Zhou

AI 速读 基于摘要

作者通过对200篇论文中371幅动机图与框架图的视觉回顾和源约束修订实验，探讨生成式AI辅助科研图表设计时解释清晰度与视觉表达之间的权衡。

cs.CV

arXiv ↗PDF ↗
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Scientific figures give research ideas a visual form through choices of abstraction, composition, and emphasis. Generative AI offers new ways to realize these choices, raising questions about explanatory clarity and purposeful visual expression. We examine these questions through a visual retrospective of 371 motivation and framework figures from 200 papers. The corpus comprises a quota-based main sample of 160 papers spanning four temporal periods and a separate purposive style-contrast sample of 40 papers. A single multimodal judge evaluates six dimensions of communication and design, with three repeated judgments per figure. We attempt source-constrained revisions of all 371 figures, of which 369 pass the recorded fidelity checks. Original-figure ratings show no uniform decline across periods. Revisions increase mean clarity ratings by 0.428 points and decrease aesthetic coherence ratings by 0.111 points on a five-point scale. Gains in the six-dimension average are larger for main-sample motivation figures than for frameworks. Case analyses show that added explanation can clarify comparisons while leaving typographic hierarchy and visual economy unresolved. These model-based findings support researcher-led design, with AI assisting exploration and authors retaining responsibility for explanatory purpose, composition, and scientific relationships. The accompanying atlas and evaluation records are available at https://ntu-ai4x.github.io/Rethinking_Figures/index_en.html.

新论文 · v1arXiv:2611.08485

接地推理在何处以及为何有用？

Where and Why is Grounded Reasoning Useful?

David Yan, Le Xue, Forrest Huang, Qiuyi Zhang, Dustin Tran, Andrew M. Dai, Yinfei Yang

AI 速读 基于摘要

作者通过思维链分析方法和消融实验，考察带坐标接地的推理在哪些视觉推理任务中真正带来收益，并提出需要不可描述视觉指代的 BrickVision 基准。

cs.CV

arXiv ↗PDF ↗
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Grounded reasoning, where a model outputs text coordinates of relevant locations in chain-of-thought reasoning, is a common strategy for solving complex visual reasoning problems. However, the benefits and limitations of grounded reasoning are understudied. We study where and why grounded reasoning is most useful. To do so, we introduce a chain-of-thought analysis method to measure how much a model uses its outputted grounding in its thinking trace and perform a detailed ablation study of a grounded reasoning training recipe. We find that grounded reasoning improves counting but shows no significant gain over baselines without coordinate grounding across a wide range of spatial reasoning and perception benchmarks. Using several synthetic tasks, we show that grounded reasoning is most useful when a task requires undescribable references, which are visual concepts that are difficult to describe in language. However, we observe that many existing visual reasoning benchmarks are solvable with only describable text references. To close this gap, we introduce BrickVision, a new benchmark that requires undescribable visual references to solve.

新论文 · v1arXiv:2611.08578

HaNDF: Object-Conditioned Geometric Neural Hand Distance Fields

Zuhao Liu, Melih Darcan, Zhengdi Yu, Riza Alp Guler, Tolga Birdal

原文摘要

We propose, HaNDF, object-conditional neural distance fields as data-driven priors for modeling the plausible hand-object configurations, in the product manifold of hand articulations. While recent unconditional generative priors showed success in modeling human pose, hands are versatile articulated bodies often found in interaction with other objects. This makes it crucial to develop a generative prior which can model the plausible hand poses conditioned on the object under interaction. To this end, our HaNDF proposes a geometry-aware, object-conditioned neural distance field, representing the plausible articulations in the zero-level set of a conditional field. We leverage tools from Riemannian geometry to (i) represent plausible hands in the zero level-set of an object-induced neural field, and (ii) project a given pose onto this field. Using HaNDF, we can also optimize for a plausible condition, determining the object under interaction. Our extensive evaluations demonstrate that our HaNDF provides a flexible and powerful prior for hand-object interaction, suitable for applications such as pose refinement, reconstruction under occlusion, and physically plausible manipulation synthesis.

cs.CV · cs.AI · cs.LG · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08478

Scientist World：为 AI 科学家构建可运行的研究世界

Scientist World: Grow a Research World for AI-Scientists

Taiming Lu, Sachin Konan, Zhuang Liu

AI 速读 基于摘要

作者提出将每篇论文转化为可执行环境，构建供 AI-Scientist 研究与训练的世界，并报告首批 1000 个已验证环境及初步评测与训练结果。

cs.AI · cs.CL · cs.LG

arXiv ↗PDF ↗
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AI-Scientists arrived, but a world for them to grow in did not. Human scientists take theirs for granted $-$ centuries of open problems and shared ideas, waiting in the scientific literature. To AI-Scientists, that world is readable but not runnable, so we build Scientist World for them: every research paper becomes an executable environment. Our autonomous pipeline extracts each paper's open-ended research question and reproduces its proposed method as the baseline; an AI-Scientist entering the environment is evaluated against what the human authors achieved. We release the first 1,000 verified environments. With the world as an evaluation, we found that frontier AI-Scientists can already surpass the human researchers 74% of the time. As a record of how AI does science, it contains 6,400 hours of research traces, with every idea, experiment, failure, and success. As a training ground, we are able to raise (+158%) a small model to match frontier models in research ideation. At last, AI-Scientists' own world has arrived, grown from and growing with science itself.

版本更新 · v3arXiv:2602.15219

多智能体家庭能源管理助手

Multi-Agent Home Energy Management Assistant

Wooyoung Jung

AI 速读 基于摘要

提出开源多智能体系统 HEMA，用大语言模型加36个领域工具支持多轮对话式家庭能源管理，并给出基于LLM模拟用户的评测框架。

cs.HC

arXiv ↗PDF ↗
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Existing home energy management systems conceptualize occupants as passive recipients of energy information and control, which limits their ability to effectively support informed decision-making and sustained engagement. This paper presents Home Energy Management Assistant (HEMA), the first open-source, multi-agent system enabling sustained human-AI collaboration - multi-turn conversational interactions with preserved context - across diverse home energy management (HEM) tasks - from energy analysis and educational support to smart device control. HEMA combines large language model (LLM) reasoning capabilities with 36 purpose-built domain-specific tools through a three-layer architecture: a web-based conversational interface, a backend API server, and a multi-agent system. The system features three specialized agents - Analysis (energy consumption patterns and cost optimization), Knowledge (educational queries and rebate information), and Control (smart device management and scheduling) - coordinated through a self-consistency classifier that routes user queries using chain-of-thought reasoning. This architecture enables various energy analyses, adaptive explanations, and streamlined device control. HEMA also includes a comprehensive evaluation framework using an LLM-as-simulated-user methodology with 23 objective metrics across task performance, factual accuracy, interaction quality, and system efficiency, allowing systematic testing across diverse scenarios and user personas without requiring extensive human subject testing. Through demonstrations using real-world household energy consumption data, we show how HEMA supports informed decision-making and active engagement in HEM, highlighting its potential as a user-friendly, adaptable tool for residential deployment and as a research platform for HEM innovation.