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新论文 · v1arXiv:2611.02520

Continuous Kinetic Model of Counterion-Mediated Redox Switching in 14-Bit Molecular Memristors for Analog In-Memory Computing

Logan Larsh, Dhiman Biswas, Nazmus Saadat As-Saquib, Su-in Yi, Thirumalai Venkatesan, Mike Banad

原文摘要

Kinetic molecular memristors offer a compelling path toward high-density analog in-memory computing by achieving 14-bit precision, wherein discrete redox transitions and counterion displacements dictate non-volatile conductance across a deterministic energy landscape. However, integrating these nanoscale kinetics into macroscale circuit simulators has remained bottlenecked by derivative discontinuities and solver convergence failures. Here, we present a physics-inspired continuous kinetic model that bridges microscopic molecular transport with scalable circuit architectures. By casting discrete intramolecular states, counterion relaxation dynamics, and Marcus-Hush-Chidsey electron transfer rates into continuously differentiable state variables, we derive a robust, Spectre-compatible compact model and a matching vectorized Python solver. This work culminates in a unified computational platform to reliably predict the behavior of complex circuits built on these molecular memristors.

cs.AR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02519

MedHEB: Benchmarking Medical Embeddings Across Heterogeneous Clinical Evidence

Yingshu Li, Shaoyang Zhou, Zhanyu Wang, Yunyi Liu, Xinyu Liang, Xi Zhang, Lingqiao Liu, Lei Wang 等 9 位作者

原文摘要

Clinical evidence is inherently relational: medical images are interpreted with prior studies, cross-modal examinations, reports, and localized findings, and retrieving relevant evidence is therefore central to clinical interpretation. Recent foundation models increasingly encode these heterogeneous sources into shared embedding spaces, yet existing embedding evaluation often focuses on isolated matches, leaving unclear whether current models retrieve relevant evidence across changes in modality, appearance, and granularity. We introduce MedHEB, a unified benchmark with 107 retrieval tasks from 54 datasets, including both in-distribution and held-out-source evaluation, across three axes: evidence form (2D images, 3D volumes, text), pairing type (image--image, image--text, text--text, and modality-bridging visual matching), and relevance granularity (global, question-conditioned, region-level). Under a single ranking protocol, MedHEB varies clinically relevant evidence definitions to test whether embedding proximity remains meaningful across heterogeneous evidence, including modality-bridging visual retrieval and region-level text-to-region retrieval. Across representative models, performance is uneven across retrieval families: models strong on conventional image--text or text--text retrieval are less reliable on modality-bridging and region-level tasks. Current embedding spaces capture some clinical relationships well but do not yet provide balanced retrieval across heterogeneous evidence. We further provide MedEmb, an MLLM-based reference embedding model trained on the MedHEB ID split, as a reproducible baseline for unified 2D--3D--text medical retrieval. We release MedHEB with preprocessing scripts, evaluation code, access manifests, and MedEmb to support research on clinically meaningful medical embeddings.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2406.16715

GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insights

Shengbo Gong, Juntong Ni, Noveen Sachdeva, Carl Yang, Wei Jin

原文摘要

Graph condensation (GC) is an emerging technique designed to learn a significantly smaller graph that retains the essential information of the original graph. This condensed graph has shown promise in accelerating graph neural networks while preserving performance comparable to those achieved with the original, larger graphs. Additionally, this technique facilitates downstream applications like neural architecture search and deepens our understanding of redundancies in large graphs. Despite the rapid development of GC methods, particularly for node classification, a unified evaluation framework is still lacking to systematically compare different GC methods or clarify key design choices for improving their effectiveness. To bridge these gaps, we introduce \textbf{GC4NC}, a comprehensive framework for evaluating diverse GC methods on node classification across multiple dimensions including performance, efficiency, privacy preservation, denoising ability, NAS effectiveness, and transferability. Our systematic evaluation offers novel insights into how condensed graphs behave and the critical design choices that drive their success. These findings pave the way for future advancements in GC methods, enhancing both performance and expanding their real-world applications. Our code is available at https://github.com/Emory-Melody/GraphSlim/tree/main/benchmark.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.07138

Can You Break RLVER? Probing Adversarial Robustness of RL-Trained Empathetic Agents

Deeraj S K, Sadhana Devarajan, Krishna Mehra, Sudhakar Mishra

原文摘要

Reinforcement learning from verifiable emotion rewards (RLVER) yields strong empathetic language models, but is evaluated with cooperative, honest simulated users. Real users gaslight, escalate, and demand unconditional validation. We introduce the Adversarial Empathy Benchmark (AEB), six adversarial trajectory types whose discriminative rewards penalize formulaic comfort, and the Emotional Consistency Score (ECS), which separates whether a model \emph{improves} the user's emotional state from whether that state stays legible in the transcript. Across eight scenario-matched conditions (480 dialogues; two RLVER and two Qwen2.5 base models, with and without thinking), RLVER-PPO-Think outperforms its untuned same-scale baseline (FS $0.979$ vs.\ $0.761$, $p<10^{-12}$) with no dialogue collapses and 53\% higher hidden-intention detection; the gain is not explained by response length. Yet under an independent cross-family judge (Gemma-3-4b-it), ECS is indistinguishable between the two ($p=0.43$). We call this an outcome--legibility dissociation: RLVER improves emotional outcomes without making the user's state more legible. We do not claim ECS measures internal understanding.

cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02517

A Study on the Effects of Finger Movements on a Humanoid Hugging Robot

Daigo Suhara, Tomoko Yonezawa

原文摘要

This study aimed to examine the impact of finger movements in hugging robots on users by experimentally investigating how the presence and patterns of such movements affect users psychologically. The results suggest that the presence and patterns of finger movements influence the sensations users seek from the robot; specifically, repetitive movements and asynchronous movements across multiple fingers tended to enhance these sensations. Additionally, simultaneous movement of all fingers and gentle hugging tended to evoke a greater sense of care. Conversely, no differences were observed regarding resistance to physical contact initiated by the robot.

cs.RO · cs.AR · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02516

Cumulative and Audited Guardrail Tool Evolution for LLM Agent Safety

Xiao Yang, Aishan Liu, Siyuan Liang, Zonghao Ying, Le Wang, Jiakai Wang, Xianglong Liu, Dacheng Tao

原文摘要

Large language model (LLM) agents increasingly operate in open-world environments where safety risks cannot be fully predefined. However, most existing guardrail mechanisms remain static, relying on fixed rules, prompts, or classifiers that generalize poorly to emerging risks. Here we present EVOLVE, a tool-level self-evolving defense framework that formulates agent security as a process of cumulative security evolution. Inspired by how human societies transform experiences into transferable protective knowledge, EVOLVE externalizes security knowledge into executable guardrail tools that are continuously inherited, independently audited, and iteratively refined through lifelong interaction. Across multiple benchmarks, backbone models, and embodied environments, EVOLVE consistently outperforms existing defenses across diverse risk scenarios with +22\% average F1 score. We further validate the framework in real-world robotic and autonomous agent systems. Our results suggest that scalable agent safety may require continuously evolving defense systems rather than static guardrails.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02512

Latent Spectral Prompting for Multimodal Hyperspectral Object Tracking

Wangshu Cai, Jun Zhou, Lei Wang, Mohammad Aminul Islam, Xu Han, Kuldip Paliwal

原文摘要

Hyperspectral videos provide rich spectral signatures for robust tracking under challenging conditions such as camouflage and low illumination. However, limited hyperspectral annotations and the large RGB-hyperspectral domain gap hinder the direct use of modern temporally pretrained RGB trackers. We propose SProTrack, a multimodal framework that transfers temporal knowledge from pretrained RGB trackers to the hyperspectral domain through latent spectral prompting. The key idea is to decouple temporal modeling from spectral adaptation, allowing temporal dynamics to be inherited from RGB foundation models while hyperspectral information is learned from unlabeled data in a self-supervised manner. Unlike band-selection methods that discard most spectral information or prompt-based approaches that rely on local-search crops, SProTrack learns a global, scene-level spectral representation from the full hyperspectral frame using an autoencoder. This latent representation is used to guide spectral prompt extraction while a pretrained RGB tracker preserves strong temporal modeling on RGB-compatible inputs. By encoding spectral-spatial structure into compact latent prompts, SProTrack bridges the RGB-hyperspectral modality gap without extensive retraining or dense supervision. Experiments on HOT2020 and HOT2023 demonstrate that SProTrack achieves the best overall performance, with strong results across all spectral domains.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02511

When Prediction Shapes the Future: A BlueSky Agenda for Reflexive Time Series Analysis

Tao Xie, Yiqun Zhang, Yiu-ming Cheung

原文摘要

Time-series models are increasingly deployed in operational systems (e.g., inventory management and clinical care) where predictions trigger interventions that alter future observations, producing records we term AI-shaped trajectories. This sequence-prediction coupling deepens as such systems evolve into agents that autonomously predict, act, and remember across long horizons. Such AI-shaped trajectories support immediate performance monitoring, but turn ambiguous once reused as passive data. An algorithm may then misread a prediction-prevented stockout as naturally stable demand, or an averted medical crisis as a false alarm. This ambiguity then propagates into everything built on the record, from retraining and agent memory to downstream predictive tasks. As a result, systems may internalize these intervention effects as natural phenomena, drifting further from the truth at every closed-loop cycle. In this Blue Sky paper, we advocate an agenda for reflexive time-series analysis that shifts the analytical object from the intervention-agnostic sequence to the prediction-action-observation loop. The agenda requires closed-loop provenance to track the operational context (e.g., predictions, policies, and actions), sequence identification to estimate how those actions affect future values under explicit causal assumptions, and closed-loop evaluation to separate intrinsic process prediction from policy-driven effects. This foundation is essential for integrating autonomous agents into such loops at scale, enabling the joint learning of intervention effects and intrinsic dynamics for robust temporal representations.

cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02510

Neuro Inspired Robotics: What Robot Learning Can Borrow from the Sensorimotor Brain

Ruihan Zhang, Davy Deng

原文摘要

Robot policies still fail at everyday manipulation: an unfamiliar task, an object never held before, a scene that changes mid-motion, a skill that should improve with use. They fail despite pretraining on over a million trajectories, pooled from dozens of robots at great expense, while a child acquires all of these abilities within a few years of learning from a single body. This survey argues that the gap is one of design, and proposes designs for the next generation of robot models, drawn from how the nervous system solves the same problems. From the body we take compliance: the policy commands stiffness alongside position, so the controller yields where the task allows and presses where it must, which greatly lowers the demand for precise position output. From the visual system, where a fast pathway for action runs in parallel with the slow pathway for recognition, we take a dedicated fast vision system for motor behavior: it precomputes the quantities the policy needs and drives a reactive policy at camera rate. From the cerebellum we take a predictor beside the policy that forecasts the consequence of each command and recalibrates from its own error. From motor cortex we take a single model that composes action units from primitives, verifies each at a sensory boundary, practices the weakest unit with a hypothesis in mind, and reasons each attempt closer to the goal. Proven primitives are then consolidated from code into weights, so they run fluently and respond quickly the next time. Together these describe a robot that acts on its own initiative, the basis of deployment and recursive self-improvement, and we offer each proposal as a hypothesis to be tested.

cs.RO · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02509

A balloon effect: ban one channel and a hidden trait finds another

Keuntae Kim, Yong Suk Choi

原文摘要

Language models trained on the outputs of other models can acquire traits that those outputs never mention, such as a preference for an animal carried by lists of numbers. Such subliminal learning can pass on unwanted traits. The intuitive defence is to find and forbid the tokens through which a trait shows, but how a trait's trace behaves under such bans, and what of it a student learns, has not been resolved. We closed a trait's outlets one by one in two stress tests, a preference chain and a name chain, across two models, six output formats and 16 traits. Bans did not remove the trait. Much as squeezing a balloon in one place makes it bulge in another, the trait moved to wherever the format still allowed, an effect we call the balloon effect. Decomposing this transfer, which bans on surface features cannot stop, down to single heads and neurons, we show that it can be cut at the circuit that writes the trait into the output. Patching that circuit while the teacher writes numbers stopped the chains, sharply reduced the overt name leak and halved the trait passed to a student, whereas the same number of random heads produced no detectable reduction. These results give a mechanistic account of how unwanted hidden traits are transferred and a route to preventing it.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02508

AttentionIndex: Accelerating Learned Sparse Attention with a Causal Inverted-File Index

Jeongmin Brian Park, Wen-mei Hwu

原文摘要

Learned sparse attention, used in production models such as DeepSeek-V4 and GLM-5, lets each query attend to only k keys chosen by a lightweight indexer, but the indexer still scores every key in the prefix. At one million tokens, this selection takes more than half of prefill time. Two-stage selection over contiguous blocks, as in HiSA, reduces this cost, but its recall declines as context grows at a fixed candidate budget. We present AttentionIndex, which routes each query over an inverted-file index that groups indexer keys by similarity and refines the candidates with the native indexer score. AttentionIndex builds the index during prefill, making new keys searchable immediately, reusing previous clustering to keep maintenance cheap, and exposing each query only to clusters built from its own prefix. On GLM-5.3 and DeepSeek-V4-Flash at one million tokens, AttentionIndex delivers 1.3-1.8 times the prefill throughput of exhaustive selection, and at throughput within 5% of HiSA, it recovers 17.0 and 10.0 percentage points more of the exhaustive top-k, respectively. AttentionIndex also improves accuracy on long-context benchmarks by up to 18.4 percentage points over HiSA at the same candidate budget.

cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02506

CellPlex: Benchmarking Single-Cell Spatial Proteomics Prediction from H&E

Zijun Gao, Yijiang Chen, Xiangde Luo, Pheng-Ann Heng

原文摘要

Spatial proteomics measures tens of proteins in every cell of a tissue section, but its cost confines it to small research cohorts, whereas hematoxylin and eosin (H\&E) slides exist for nearly every patient. Inferring single-cell spatial proteomics from H\&E would bring its analyses to routine pathology, and a growing number of virtual staining models pursue this goal. Each is evaluated on its own cohort, panel and pixel-level metric, and released models transfer poorly to new cohorts, leaving open which approach to build on and which proteins H\&E can reveal. We introduce CellPlex, a benchmark that poses the task at the level of the cell: seven cohorts with same-section H\&E and multiplexed immunofluorescence, from colorectal and breast cancer to lymph-node metastases, bone marrow and glioma, with 717 samples, 44.8 million cells and 149 proteins, processed by one pipeline into per-cell targets. Pathology foundation models (PFMs) are adapted with one cell-level protocol, specialist models are fine-tuned under a matched budget, and all are scored on the same cells. Adapted PFMs are the most accurate models on every cohort, ahead of the specialists even after fine-tuning, but keep less than half of their accuracy on cohorts they were not trained on. Which proteins can be predicted depends on the tissue, not on how many cells were measured for them. By fixing the cells, targets and splits on which predictions are judged, CellPlex sets a common standard for inferring costly molecular measurements from routine H\&E and shows how far H\&E can stand in for spatial proteomics. We will release the data, splits, evaluation code and baselines as a testbed for developing and comparing future pathology foundation models and virtual staining methods.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.01055

MMCOMET: A Large-Scale Multimodal Commonsense Knowledge Graph for Visual Reasoning

Jinwoo Kim, Rina Carines Cabral, Dhita Putri Pratama, Eileen Wang, Hiba Arnaout, Dhita Pratama, Shuo Yang, Dangyang Liu 等 14 位作者

原文摘要

Commonsense knowledge is widely used to support multimodal reasoning and generation, yet existing resources are predominantly text-only and lack grounding in visual context. This limitation often leads to the retrieval of semantically related but visually misaligned knowledge, and can degrade downstream performance. We introduce MMCOMET, a large-scale multimodal commonsense knowledge graph that integrates physical, social, and eventive knowledge with visual grounding. MMCOMET extends ATOMIC2020 by associating each commonsense triple with representative images through a hybrid alignment pipeline, resulting in over 900K multimodal tuples spanning 19 relation types. We further establish a unified evaluation framework across diverse vision-language tasks, including visual commonsense reasoning, visual question answering, visual storytelling, image editing, and image captioning. Our analysis shows that multimodal grounding provides more contextually aligned and reliable knowledge compared to both text-only knowledge graphs and LLM-based knowledge generation, particularly in reasoning-intensive scenarios. At the same time, we observe that the effectiveness of external knowledge is task-dependent, and naive knowledge integration can introduce noise in perception-dominated settings. To mitigate this, we propose an adaptive knowledge router that dynamically selects optimal paths or bypass knowledge in harmful settings. MMCOMET provides a scalable multimodal commonsense resource and a benchmark for studying the role of grounded knowledge in multimodal systems.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02505

QFacePlacement: From Arbitrary Tensorization to Semantic Spatial Correspondence in Modern Face Verification

Krishna Bhatia, Kavita Choudhary, Pruthwik Mishra, Praveen Kumar Chandaliya

原文摘要

Quantum-inspired computer vision often reshapes high-dimensional features into tensors and applies tensor-network models. In face verification, this can be misleading: modern embeddings are optimized for cosine similarity, and tensorization alone does not establish the low-rank structure needed for tensor trains or matrix-product operators (MPOs). We study model placement through two controlled interfaces. First, cross-model embedding alignment requires a 512 x 512 operator to map AdaFace embeddings into ArcFace space. Dense Ridge and Procrustes mappings remain strong, while deterministic TT-SVD and MPO compression underperform even with basis changes and low-rank residuals. At a 64K-parameter storage budget, mean directional AUC is 0.9297 for dense Ridge, 0.8952 for rank-64 SVD, 0.8685 for the best low-rank-MPO hybrid, and approximately 0.649 for raw MPO. Second, before global pooling, we construct a 7 x 7 x 7 x 7 probe-gallery correspondence tensor from local AdaFace descriptors. A geometry-aware symmetric tensor train with bond dimension 2 uses 122 trainable parameters, compared with 2,403 for a dense linear matcher, while attaining an LFW AUC of 0.9802. Yet compression does not translate into better low-false-acceptance performance: on CFP-FP, TAR at FAR = 0.001 is 0.0102 versus 0.1734 for global cosine matching, and on CPLFW it is 0.7805 versus 0.8737. These results suggest that quantum-inspired factorization is most credible when the chosen interface provides task-aligned axes and compressible structure. They also show why aggregate AUC must be assessed alongside operational low-FAR metrics.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02503

Towards Multi-Value Alignment via Value Localization in LLMs

Xueqi Ma, Yanbei Jiang, Xingjun Ma, James Bailey, Sarah M. Erfani

原文摘要

Aligning large language models (LLMs) with multiple, potentially conflicting human values remains challenging, as existing multi-objective alignment methods often require costly fine-tuning and provide limited insight into the internal mechanisms underlying value-related behaviors. In this work, we investigate multi-value alignment from a mechanistic interpretability perspective by identifying value-sensitive attention heads (value heads) in LLMs. Using a probing-based value localization framework, we discover sparse sets of attention heads associated with diverse dimensions, spanning both human values (e.g., harmlessness, honesty) and stylistic preferences (e.g., coherence, complexity). Analyses across multiple LLM families reveal functional specialization and structured interactions among value heads, including conflicts between certain values. Targeted head ablations further induce substantial, value-specific behavioral changes, providing causal evidence of their functional importance. Building on these findings, we propose an inference-time activation steering strategy that enables controllable alignment with individual values or combinations of values without updating the underlying model parameters, supported by theoretical analysis. Experiments across multiple value alignment benchmarks demonstrate effective single- and multi-value control while largely preserving non-target model performance. Our findings connect mechanistic interpretability with flexible multi-value alignment, offering a foundation for more interpretable and pluralistic LLM behavior. The code is available at https://github.com/sihuo-design/ValueHeads.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02501

SpecPilot: Autonomous Algorithm Discovery and Systems Optimization for Speculative Decoding

Kunjun Li, Wenhao Chai, Yihao Liang, Yuezhou Hu, Shuyao Xu, Yi Pan, William X. Zheng, Kannan Ramchandran 等 10 位作者

原文摘要

Speculative decoding accelerates memory-bound LLM decoding with a lightweight drafter, and parallel drafters extend this speedup by drafting a whole block in one forward pass. However, it remains unclear whether their released systems (stock) attain the full speedup that parallel drafting permits. We identify three challenges. First, with one request, each step accepts few tokens but reads every target weight. Second, with batching, per-request verification work roughly halves the speedup over autoregressive (AR) decoding. Third, speculative decoding is deployment-specific, and no universal optimization fits every serving regime, drafter, target model and GPU. To address these challenges, we present SpecPilot, an autonomous agentic loop that discovers algorithms and systems optimizations for each deployment. In each round, a cost model locates the largest gaps between the current system and its ceiling. Parallel agents explore directions and integrate the successful ones. Token forcing checks every output token against stock to detect reward hacking. A structured memory carries validated mechanisms to new rounds and deployments. SpecPilot discovered a general lossless 12-bit weight format, tree verification for hybrid recurrent targets, two-level verification and three batching mechanisms. We evaluate it across four target models, three drafters, two GPU platforms and five benchmarks. Without training or quality loss, it raises the speedup over AR from stock's 4.46$\times$ to 10.87$\times$ with one request, and from 1.94$\times$ to 3.93$\times$ with batching.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02499

Fast-Slow Evolutionary Occupancy Prediction via Controlled Dynamics

Runhe Yang, Zhiyuan Zhou, Yuxiang Yan, Xuetong Yang, Yikai Pan, Jiacheng Tang, Zhuolin He, Jianhua Han 等 10 位作者

原文摘要

3D semantic occupancy prediction provides dense geometric and semantic scene representations for autonomous driving, where both high accuracy and low response latency are crucial for safe downstream forecasting and planning. Existing methods usually exhibit different accuracy-latency characteristics. Performance-oriented models provide stronger geometric and semantic predictions, while deployment-friendly models provide faster responses. This motivates EvoOcc, a fast-slow evolutionary occupancy prediction framework that jointly exploits their complementary strengths to improve the accuracy-latency balance without changing the upstream architectures. EvoOcc models evolutionary occupancy prediction as a continuous time controlled dynamical system, enabling flexible adaptation to variable slow system delays and irregular fast system arrivals. The framework further decomposes the evolution process into dual state evolution where ego state evolution compensates for the deterministic coordinate shifts and scene state evolution models the genuine geometric and semantic scene changes. Extensive experiments on three different fast-slow system pairs and irregular prediction arrivals show that EvoOcc consistently improves over fast system baselines while maintaining much lower latency than the slow system, indicating a favorable accuracy-latency balance and stable behavior under practical inference situations.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02498

Semantic Caches for LLM Web Services Leave Their Error Budget Unspent

Hanshuai Cui, Qianli Ma, Zhiqing Tang

原文摘要

Web services built on large language models (LLMs) receive many paraphrases of earlier queries, which a semantic cache answers from stored responses. A reused answer can be wrong, so wrong answers must stay below a share $δ$ of the queries. Existing caches reuse above a fixed similarity threshold or, like vCache, learn a per-entry rule that bounds each query's error by $δ$. However, fixed thresholds break the budget on new streams, and per-entry rules need labels on every entry before reusing it. We find an unspent error budget, as vCache realizes only 12-55% of $δ$ on three public query streams. Surplus spends this budget where reuse is most likely correct. It ranks queries by a risk score shared across paraphrase clusters and opens risk bins as far as an audited error estimate allows. Free labels from every LLM call and a ledger of realized errors keep this threshold safe under drift. On five streams, including ORCAS search queries, Surplus holds $δ$ in every test cell, which no baseline does. On the shuffled streams it serves 40% more hits than vCache, and its pooled hit rate beats the best baseline holding $δ$ in each cell by 4.10 points. In a vLLM testbed it cuts the mean latency by 31.59% at the highest load, against 23.67% for vCache.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02497

RoboColosseum: Evaluating Physical Intelligence at Scale In-The-Wild

Tan Sang Nguyen, Yi Ru Wang, Shailesh Shailesh, Gang Yang, Jiamo Hu, Jianuo Jiang, Haoquan Fang, Zhongzheng Ren 等 27 位作者

原文摘要

General-purpose robot policies must work across robots, tasks, and environments, yet the variation that makes physical trials informative also makes them difficult to reproduce. We introduce RoboColosseum, a distributed evaluation framework that combines unified policy serving and standardized protocols across a network of locally operated robots in diverse environments. Our initial study evaluates five vision-language-action (VLA) policies and three frontier AI agents across four embodiments (SO-101, Franka, Bimanual-YAM, and G1 Humanoid). The Open Track compares models without task-specific adaptation in blinded, pairwise trials under matched local conditions. The Fine-tune Track measures how well VLA policies adapt to 20 tasks across all four embodiments when each policy receives the same 100 demonstrations per task. Across 5,699 pairwise Open Track comparisons at 124 sites, frontier agents rival specialized VLA policies, while all systems struggle to complete open-world tasks reliably. After fine-tuning, GR00T N1.7 leads on SO-101 and pi0.5 leads on Franka and Bimanual-YAM. Policy rankings show weak agreement across embodiment-specific task suites (Kendall W = 0.24). Open Track rankings poorly predict relative performance after fine-tuning, with substantial rank reversals on all three embodiments shared between tracks. Measuring partial progress reveals differences that task success alone misses. On the G1 Humanoid, no policy completes any of the five tasks, yet average progress ranges from 13.3% to 23.2% across policies. Open Track rankings stabilize as trials accumulate, and active matchmaking achieves comparable ranking agreement with substantially fewer physical trials than uniform sampling. These results show how distributed physical evaluation can reveal where robot policies generalize, how well they adapt to new tasks, and how far they progress on tasks they fail to complete.

cs.RO · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.22174

OpenHLM: An Empirical Recipe for Whole-Body Humanoid Loco-Manipulation

Yingdong Hu, Haodong Zhu, Boyuan Zheng, Yihang Hu, Tong Zhang, Zunhao Chen, Junming Zhao, Ruiqian Nai 等 9 位作者

原文摘要

Whole-body humanoid loco-manipulation requires coordinating the robot's entire kinematic chain. However, most existing systems typically decouple the upper and lower bodies into separate controllers, limiting such coordination and yielding behaviors similar to those of a wheeled dual-arm platform. In this paper, we ask what it takes to build a whole-body native vision-language-action (VLA) model that maps language and pixels directly to all of the humanoid's degrees of freedom. We conduct a systematic empirical study organized as a roadmap of one-variable-at-a-time experiments across three phases: whole-body teleoperation, VLA model design, and heterogeneous co-training. Our study yields several intriguing findings: a joint-based whole-body teleoperation interface outperforms alternatives that only partially expose the humanoid's degrees of freedom; a VLA pretrained on static and wheeled dual-arm platforms transfers surprisingly well to a humanoid's full action space; and co-training with HuMI, the humanoid analog of UMI, extends the policy to new objects and instructions without additional whole-body teleoperation on those targets. Following this roadmap yields OpenHLM, an open-source recipe for whole-body humanoid loco-manipulation. In a challenging long-horizon task that spans a wide vertical range of the humanoid, OpenHLM outperforms two state-of-the-art humanoid VLA baselines (GR00T N1.6 and $Ψ_0$) using less than half the total demonstration time. Our code, training data, and model checkpoints are available at [https://openhlm-project.github.io/].

cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.38249

Keepace.ai: A Technical Report on a Large Language Model for Sports and Health

Keepace. ai Team, Bowen Wu, Heng Wang, Jufeng Mi, Kang Liu, Kun Li, Long He, Manling Han 等 15 位作者

原文摘要

Large language models hold promise for delivering safe, scientifically grounded, and personalized exercise guidance at scale, yet the sports-and-health domain still lacks curated training corpora and rigorous evaluation benchmarks. Keepace (Keepace.ai) is a domain-specialized LLM built on Qwen3.5-27B through post-training, currently optimized for Chinese-language exercise guidance across three tasks: workout plan generation, sports-and-health knowledge QA, and personal sports data analysis. In internal rubric-based evaluations designed with domain experts, Keepace outperforms its base model and larger general-purpose baselines, with the most pronounced improvements in exercise safety, boundary compliance, structured output completeness, and scientific accuracy. This report summarizes the current design, data pipeline, safety-first evaluation framework, and evaluation results of Keepace.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.29319

Rethinking Stepwise Model Routing: A Cost-Efficient Table Reasoning Perspective

Shenghao Ye, Yuxiang Wang, Yu Guo, Tao Zhang, Shuangwu Chen, Zijian Wen, Qihui Zhu, Mengjie Zhang 等 11 位作者

原文摘要

Large Reasoning Models (LRMs) achieve strong performance on table reasoning tasks but incur substantial inference cost due to long reasoning traces. Stepwise model routing mitigates this issue by dynamically assigning reasoning steps to smaller or larger models. However, stepwise model routing for table reasoning remains underexplored. Through empirical analysis, we find that reasoning steps involving tables contain two types of tokens with distinct uncertainty distributions: table tokens grounded in table structure, such as cell values and headers, and text tokens representing surrounding natural-language reasoning. The uncertainty of both token types is correlated with the risk that the model makes an error in the next reasoning step. However, existing methods fail to model them separately, leading to suboptimal routing decisions. To address this, we propose EcoTab, a table-aware stepwise routing framework for efficient table reasoning. At each reasoning step, EcoTab separately estimates the uncertainties of table tokens and text tokens, maps them to next-step failure risks for the small model, and combines the two risks for routing. Experiments on multiple table reasoning benchmarks show that EcoTab consistently outperforms strong baselines and achieves a better balance between accuracy and efficiency.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.33702

Seeing the Options: What Should a Visual Candidate See?

Fengxin Li

原文摘要

Agents often choose among candidates that are images: elements of a screenshot, product photos, charts. A single-pass decision model either encodes the request and all candidates as one sequence, making its answer depend on candidate order, or encodes each candidate alone, which keeps order invariance and reusable encodings but hides the request from the candidate. We ask where the joint encoder's advantage comes from: from candidates seeing the request, or from candidates seeing each other while they are encoded. With one backbone and the same fine-tuning steps on screenshot crops of Multimodal-Mind2Web, the answer is mostly the request. Continuing each candidate from a forked cache of the encoded request recovers 11 to 13 of the 12 to 17 points that independent encoding loses to joint encoding, at the price of cross-request reuse, and we prove that order invariance stays exact whenever candidates never see each other. A joint encoder retrained with its candidates isolated from one another during encoding loses 1.1 and 0.4 ranking points in one paired run (0.7 and 0.3 against the mean of five joint runs), and permutation-equivariant interactions among candidates, over pooled vectors or inside the encoder, gain nothing beyond continued training. At a matched abstention operating point the state-conditioned model is 0.6 and 1.0 points below the joint encoder (mean of four runs) with a flip rate of zero, against 8-10% for the joint encoder; on AndroidControl crops (three and five runs) it is 2.5 to 4 ranking points below. Finally, the abstention threshold drifts: the choice term of the loss ignores the score level, and the coverage term pins it only weakly once positives saturate; a quantile threshold aligns operating points.

cs.AI · cs.CV

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2509.22964

Functional Critics for Off-Policy Actor-Critic: From Stability to Exploration

Qinxun Bai, Yuxuan Han, Yuxiao Wen, Wei Xu, Zhengyuan Zhou

原文摘要

Actor-critic methods are central to modern reinforcement learning, yet off-policy actor-critic theory remains far removed from practical algorithms, due in part to the interaction between the deadly triad and the continually changing target policy. We revisit functional critics, i.e., value functions that explicitly condition on the policy, and show that this representation provides a shared foundation for off-policy training stability and policy-space exploration. We first develop a convergent off-policy actor-critic framework under linear function approximation that accommodates target-based TD, partial coverage, and evolving behavior policies. Our analysis combines a policy-evaluation guarantee under partial coverage with policy-gradient generalization of functional critics to yield a dual trust-coverage framework that controls policy-gradient error under evolving behavior policies while enabling greater reuse of off-policy data. We further develop a replay-buffer analysis under i.i.d. sampling, yielding a principled replay-sampling design for minimizing the replay-sample complexity required to reach a prescribed evaluation accuracy. We next show that functional critics provide a natural representation for model-free posterior-sampling-style exploration. Standard critic ensembles do not naturally represent joint uncertainty over return functions across policy space, whereas functional-critic ensembles represent policy-conditioned return functions directly. This motivates a particle-based, model-free analogue of posterior-sampling-style exploration without explicit model learning. Finally, we instantiate these ideas in a deep functional actor-critic algorithm that, despite omitting several standard off-policy AC heuristics, achieves strong performance across the full Dog and Humanoid task series of the DeepMind Control Suite.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02491

Same Sentence, Different Construct: Identity-Swapped Rationales Reveal Measurement Non-Invariance in LLM Hate Speech Classifiers

Wenchang Wang, Yue Song

原文摘要

Large language models are increasingly used as measurement instruments for socially consequential constructs such as hate speech, and are typically validated by their aggregate agreement with human labels. A valid instrument must also apply the same construct to every group it measures. We test this requirement using 421 \hc{} templates that are instantiated identically for seven protected groups, so that only the identity term varies. Four LLM classifiers each exceed 0.95 balanced accuracy, yet within identical templates two of them miss hateful statements about women at markedly higher rates than the same statements about other groups (excess false-negative rates of 8.3 and 4.4 percentage points), a gap first reported for fine-tuned classifiers in 2021. We show how the construct shifts. Comparing rationales within each template, we find that models invoke protected-group status for other groups but recast the identical statement about women as personal dislike or as aimed at an individual, in 22 of 30 missed templates for one model. Adding the benchmark's own definition, which lists sex as a protected characteristic, does not close the gap, and the contrast persists. Apparent false-positive disparities, by contrast, largely vanish under identity control and trace to reclaimed slurs present only for some groups. Errors shared by all models concentrate where model and benchmark definitions diverge, and pairwise counterfactual accuracy adds little beyond endpoint accuracy. We argue that validating LLM measurement requires identity-controlled tests of measurement invariance that examine both decisions and the criteria models state for them.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.41187

Multiple Swap Local Search Approximations for Uniform-Cost Capacitated Facility Location with Extensions to Outliers

Zachary Friggstad, Pengfei Wu

原文摘要

We study \textsc{Capacitated Facility Location}, a fundamental clustering problem, in the setting of uniform opening costs and non-uniform capacities. We give a polynomial-time $(3+ε)$-approximation for this problem. Our algorithm is a multiple-swap generalization of the local search algorithm by Dabas, Garg, and Gupta (2024), who obtained a $3.732$-approximation. The main technical contribution is the analysis of this natural multiple-swap local-search method: variants of this approach have been analyzed previously, but allowing larger swaps requires new grouping and counting arguments. We also show that a multiple-swap local search algorithm provides a $(3+ε)$-approximation for \textsc{Capacitated Facility Location with Outliers}, again in the same setting of uniform opening costs and non-uniform capacities. This is an improvement over the $6.372$-approximation, again by Dabas, Garg, and Gupta. Finally, we show that the factor $3$ is tight for our local-search framework, and that the same multiple-swap idea does not improve the known locality gap for the non-uniform opening-cost setting.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02489

Cross-Domain Joint-Embedding Pretraining For Heterogeneous Industrial Time Series

Jonathan Adam Rico, Nagarajan Raghavan, Senthilnath Jayavelu

原文摘要

Industrial predictive maintenance and manufacturing tasks frequently contain abundant sensor measurements, but with limited labeled data. We investigate cross-domain self-supervised pretraining using joint-embedding predictive architecture for heterogeneous industrial time series (JEPA-HITS), which learns one encoder from various domains such as battery, semiconductor chemical mechanical planarization (CMP), computer numerical control (CNC) machining, and bearing while preserving dataset-specific temporal scales. We evaluate representation reuse on regression and classification problems under reduced-label downstream training, and leave-one-domain-out (LODO) pretraining. Compared with the same architecture initialized randomly, LODO pretraining reduces RMSE from 10.72 to 7.95 on battery remaining useful life (RUL), 4.34 to 3.89 on CMP, and 13.53 to 11.72 on tool wear regression, while improving bearing fault diagnosis macro-F1 from 0.547 to 0.737. Overall results suggest that unlabeled time series measurements from heterogeneous industrial processes can provide reusable representations for downstream learning with limited labeled data.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.09959

SEAMLiS: Visibility-Aware Safety for Perception-Limited Multi-Robot Exploration

Taekyung Kim, Rahul H Kumar, Aswin D. Menon, Tzu-Hsiang Lin, Dimitra Panagou

原文摘要

Autonomous exploration in unknown environments is typically driven by informative frontiers, viewpoints, or trajectories, while local safety controllers avoid obstacles represented in the current map. Under finite sensing range and limited field of view, this separation can be unsafe: an exploration stack may plan optimistically through unobserved space and steer the sensor toward information gain rather than along the direction of motion, causing hidden obstacles to be detected too late for bounded-actuation avoidance. This paper presents SEAMLiS (Safe Exploration for Autonomous Multi-Robot Systems Under Limited Sensing), a modular execution-layer safety framework for decentralized multi-robot exploration. SEAMLiS preserves the upstream exploration stack, including the goal allocator and local planner, and enforces safety at the execution layer through perception-aware attitude and positional filters. A gatekeeper-based attitude filter switches between a visibility-promoting yaw policy and a velocity-tracking backup policy to preserve visibility of the critical known-free/unknown boundary with sufficient braking margin. A Control Barrier Function (CBF)-based positional filter then avoids known obstacles, newly detected obstacles, and other robots. We provide sufficient collision-avoidance conditions and validate the framework in randomized simulation, Isaac Sim, and Crazyflie hardware experiments. Results show collision-free exploration across tested single- and multi-robot settings while retaining much of the efficiency of visibility-promoting yaw control.

cs.RO · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.02358

Transfer Learning Through Conditional Quantile Matching

Yikun Zhang, Steven Wilkins-Reeves, Wesley Lee, Aude Hofleitner

原文摘要

We introduce a transfer learning framework for regression that leverages heterogeneous source domains to improve predictive performance in a data-scarce target domain. Our approach fits a conditional generative model to each source domain and calibrates a shared linear combination of the generated responses via conditional quantile matching at the observed target covariates. The calibration aligns the conditional response distributions while accommodating simultaneous shifts in both the covariate and conditional response distributions. The calibrated responses then serve as pseudo-labels to augment the target data for a broad class of downstream learning tasks. Theoretically, we derive an excess risk bound for an empirical risk minimizer (ERM) trained on the augmented data, separating the benefit of an enlarged training sample from errors due to density ratio estimation, source generator estimation, conditional calibration, and irreducible transfer bias. Empirically, simulations and real-data applications show that the proposed method can improve target-domain prediction when the transfer bias is small.

stat.ML · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02487

RecRSI: Mechanism-Guided Discovery of Recommendation Algorithms

Tingran Gan, Zhouziyue Lu, Dian Jin, Tao Li, Yang Gao, Ming Liu, Weiwen Liu, Jianghao Lin 等 10 位作者

原文摘要

Improving recommendation algorithms for the Web requires coordinating interacting design choices and learning from experiments. We present RecRSI, an LLM-based research framework that uses mechanism hypotheses to organize algorithm discovery. Each hypothesis links a proposed mechanism change to a reference design, a rationale, and an expected observation. MechSpace derives implementable primitives and composition rules from the task source; Research Line uses this space and prior evidence to develop and test hypotheses; Mechanism Feedback carries outcomes and their design context into subsequent research. This organization makes each experiment inform which mechanisms to develop, revise, or combine next. Across four initial recommenders and four datasets, RecRSI yields average relative improvements of 8.48% in NDCG@5 and 6.88% in HR@5 over the initial algorithms, and 2.60% and 2.20%, respectively, over the strongest competing result for each setting and metric. Component and process studies link these gains to coordinated mechanism choices and evidence-guided follow-up. Our code is publicly available at https://github.com/DynamisG210/RecRSI.

cs.IR · cs.AI

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arXiv ↗PDF ↗