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新论文 · v1arXiv:2611.02605

StateFence: Task-State Protection Against Evidence-Chain Contamination in Blue-Team LLM Agents

Haowen Dai, Alejandro Guerra-Manzanares

原文摘要

Indirect prompt injection creates a distinctive risk for blue-team Large Language Model (LLM) agents in Security Operations Centers (SOCs). Malicious instructions can be embedded in telemetry, tickets, retrieval results, or memory and then propagate through alert triage, incident summarization, response recommendation, and Indicator of Compromise (IOC) extraction as locally plausible intermediate decisions. We characterize this failure mode as evidence-chain contamination of protected task state. The main challenge is task dependent: a suspicious string may be malicious control text for summarization but essential evidence for IOC extraction. To address this challenge, we propose StateFence, a task-aware evaluation and defense framework that treats severity labels, incident narratives, response policies, and IOC sets as protected states. StateFence maps indirect prompt-injection attacks into explicit, stealth, multi-hop, and persistent SOC evidence attacks, records observable propagation traces, and routes defenses according to the attacked state. Across controlled SOC cases and public-SOC validation cases, StateFence lowers attack success and reduces failures that appear only after evidence is poisoned, while preserving task utility and avoiding the indicator loss caused by task-mismatched filtering. Our findings indicate that robust blue-team LLM agents require mechanisms that preserve task-critical state under untrusted evidence.

cs.CR · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02603

BGD-SF PolySegNet: boundary-guided dynamic selective fusion network for robust polyp segmentation in colonoscopy images

Nishi Kanta Paul, Shamia Maherin, Morsheda Akter, Moinul Hossain, Md Kishor Morol

原文摘要

Effective polyp segmentation in colonoscopy images is crucial for early detection of colorectal cancer, a leading cause of cancer-related death worldwide. While current deep learning models perform well on traditional Dice benchmarks, border segmentation remains difficult. Most methods use boundary information only as an additional loss signal during training, which prevents the network from explicitly learning border-guided feature selection or mask refinement. This paper introduces BGD-SF PolySegNet, a boundary-guided encoder-decoder architecture that controls three phases of the segmentation pipeline: dynamic multi-scale context aggregation, selective skip-feature fusion, and final mask refinement. The architecture comprises a pretrained EfficientNet-B4 backbone and three proposed modules: Boundary-Guided Dynamic Cascaded Multi-Scale Context Aggregation (BGD-CMSCA), Boundary-Guided Selective Attention Fusion (BG-SAGF), and a Multi-Level Boundary Guidance Head (MBGH). The model is trained on 3,535 annotated images from Kvasir-SEG, BKAI-IGH NeoPolyp-Small, and PolypGen, and evaluated on four held-out benchmark datasets. On the internal test set, BGD-SF achieves a Dice of 0.887, IoU of 0.839, mask-derived Boundary F1 of 0.531, HD95 of 30.32, and ASSD of 18.10, improving Boundary F1, HD95, and ASSD by 15.7%, 27.5%, and 34.6% over the strongest CNN baselines. External validation on CVC-ClinicDB, CVC-ColonDB, ETIS-Larib, and CVC-300 yielded Dice scores of 0.891, 0.776, 0.863, and 0.885, respectively. The model runs at 35 FPS with 22.51M parameters. Wilcoxon signed-rank tests confirm statistically significant mask-derived Boundary F1 improvements over six of seven ablation variants (p < 0.001; small-to-moderate effect sizes, r = 0.215-0.434), demonstrating that integrating boundary guidance as an active architectural component measurably advances polyp boundary delineation.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02601

Parallelism-Aligned Pruning and Round-Aware Context Reuse for On-Device Multimodal Agents

Peipei Jiao, Mingkang Liu, Weiming Meng, Lichen Pang, Tianyuan Liu, Junyan Li

原文摘要

On-device multimodal agents are limited by two deployment mismatches: pretrained attention structures may not divide evenly across the compute cores of an edge accelerator, causing irregular partitions; and standard prefix caching keeps historical visual tokens in the persistent KV cache, whose capacity grows with the number of rounds. We present PARC, which combines parallelism-aligned structured pruning with lifetime-aware KV-cache reorganization. At the model level, it uses first-order Taylor importance and grouped-query-attention-aware selection to make attention dimensions divisible by the target core count while preserving the query-to-key/value ratio and head dimension. At runtime, it separates the system prompt, persistent text history, and transient visual observations by lifetime, updating text history during tool execution so that historical images leave the persistent cache and more interaction rounds fit a fixed budget. On 2,062 GUI actions, the incremental interface shows no accuracy regression, and task-aware recovery training restores 98.7% of the action-type accuracy gap and the coordinate hit rate to the original level. Under seven-core deployment, hardware-aligned pruning reduces vision-encoder latency by 11.4%, improves decoding throughput by 23.8%, and lowers per-heap high-water memory by 12.7%; a parameter-matched control confirms that the speedup stems mainly from core alignment rather than parameter reduction.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02600

W2A: Distilling World Priors into Action Models for Real-Time Robotic Control

Xiaopeng Zhang, Yongjin Mu, Yueyang Weng, Chengzhi Zhao, Yanjie Li

原文摘要

World Action Models (WAMs) have recently shown great promise in robotic control, benefiting from the future imagination capability of large-scale pretrained video models. However, most existing WAMs require explicitly imagining future states at test time, incurring substantial computational overhead and latency that make them unsuitable for real-time robotic control. In this paper, we propose W2A, a two-stage distillation framework for real-time robotic manipulation that distills intermediate denoising representations from a video diffusion model into an action model during training, while entirely discarding the video model at test time. We hypothesize that although video latent representations are high-dimensional, a compressed low-dimensional subspace is sufficient to provide effective supervision for action generation. This makes it possible to transfer such information effectively into an action model. Extensive experiments in both simulation and real-world settings demonstrate competitive success rates while significantly improving inference efficiency. On LIBERO, W2A achieves a 95.9% average success rate. More importantly, W2A achieves about 21x faster inference than the representative WAM baseline.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.19168

Beyond Safe Data: Pretraining-Stage Alignment with Regular Safety Reflection

Jinhan Li, Kexian Tang, Yihan Xu, Zhuorui Ye, Kaifeng Lyu

原文摘要

To achieve deeper safety alignment for large language models (LLMs), recent efforts have studied how to push safety interventions earlier into the pretraining stage, primarily by filtering unsafe data or rewriting it into safer forms. We argue that pretraining-stage alignment should go beyond making the data safe: LLMs may generalize unsafe behaviors from seemingly benign pretraining data. To this end, we propose Safety Reflection Pretraining, a pretraining-stage alignment method which regularly inserts short safety reflections between sentences in pretraining corpora. This integrates self-monitoring directly into language modeling, establishing a foundational capability that is subsequently reinforced by compatible post-training. Our experiments with 1.7B models pretrained on FineWeb-Edu show that Safety Reflection Pretraining improves safety classification accuracy and substantially reduces the success rates of inference-stage and fine-tuning attacks. Complementary to our real-world experiments, we also introduce a fully controlled synthetic environment, MedSafetyWorld, with a clear definition of safety and a reasoning structure under which we find that models can easily generalize unsafe behaviors from safe data. Ablations in MedSafetyWorld further demonstrate a clear advantage of Safety Reflection Pretraining over data filtering and rewriting in preventing models from acting on unsafe behaviors. Taken together, our findings suggest that pretraining alignment should not only make the training data safe, but also shape the behaviors that models are likely to acquire from safe data.

cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02599

Camera-Free Object Discovery and Interaction with GazeTags

Vimal Mollyn, Chris Harrison

原文摘要

We present GazeTags, a camera- and computer-vision-free approach for worn head-gaze object awareness. Our small electronic tags cost roughly \$2 each and can operate for more than a year on a coin cell battery. They work in concert with a user-worn device (smartglasses, hearables, etc.) that periodically emits a focused infrared signal aligned with the user's head direction. Only GazeTags within that narrow head-oriented beam wake and respond, providing their identity and optional payloads such as object state or live sensor readings. By avoiding cameras and computer vision pipelines, our approach is very energy efficient, able to operate continuously even in battery-constrained wearables, and sidesteps non-trivial privacy issues. We describe our hardware design, characterize our system performance and operating tradeoffs, and demonstrate how GazeTags can enable practical gaze-aware environments.

cs.HC · cs.CV · cs.GR · cs.MM

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2507.16221

Unbeatable imitation of a friend

Masahiko Ueda

原文摘要

Imitation sometimes achieves success in multi-agent situations even though it is very simple. In repeated games, success of imitation has been characterized by unbeatability against other agents. Previous studies specified conditions under which imitation is unbeatable against co-players in repeated symmetric games, and clarified that the existence of unbeatable imitation is strongly related to the existence of payoff-controlling strategies, called zero-determinant strategies. However, these results were on the situations in which one repeated game is played by one group of players. It was pointed out that imitation of other players in the same group and imitation of other players in the same role in other groups generally result in different outcomes. Here, we investigate the existence condition of unbeatable imitation in the latter situations, described by two concurrent repeated games played by two different groups of players. We find that the existence condition of unbeatable imitation is stronger than the existence condition of unbeatable zero-determinant strategies, whereas both are very limited. Furthermore, we find that adding exploration can make an imitation strategy unbeatable even in games where imitation without exploration can be beaten. Our findings suggest a strong relation between unbeatable imitation and payoff control even in the two concurrent repeated game situations.

physics.soc-ph · cs.MA · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02593

AVID: A 5T fMRI Dataset for Benchmarking Auditory-induced Visual Mental Imagery Decoding

Shiqi Shen, Shurui Li, Xilin Zhang, Yuanning Li

原文摘要

Visual mental imagery (VMI) decoding uses brain activity to reconstruct internally generated visual scenes, providing a potential pathway for externalizing internal experiences and developing neuroprosthetic communication interfaces. However, progress is limited by the lack of dedicated, training-scale VMI datasets. Current VMI paradigms often rely on visual prompts or short-term memory images, introducing visual and mnemonic confounds that make it difficult to disentangle neural activity associated with endogenous image construction from signals driven by prior or concurrent visual input. Furthermore, existing VMI datasets offer limited descriptive control over individual visual attributes, making it difficult to systematically assess fine grained visual details. To address these gaps, we introduce AVID, the first dedicated training-scale 5.0 Tesla functional magnetic resonance imaging (fMRI) resource for auditory-induced VMI decoding. AVID comprises approximately 67 hours of dense recordings from nine participants. The dataset is structured into two complementary splits: a scene-level complex split using naturalistic Mandarin descriptions and a controlled simple split with explicitly specified visual attributes. We further formalize a standardized evaluation protocol that decouples source-image consistency from semantic alignment, allowing for a more nuanced assessment of imagery reconstruction. Baseline qualitative and quantitative results show that current reconstruction pipelines remain limited on AVID, although the most successful reconstructions preserve recognizable scene level semantics from spoken descriptions. By combining dedicated training-scale neural recordings and standardized evaluation resources, AVID provides both a data foundation for imagery-specific model development and a benchmark for evaluating VMI decoding with substantially reduced sensory contamination.

cs.CV · cs.AI · q-bio.NC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02587

They Know Where It Is, But Not What It Says: A Bilingual Hallucination Benchmark of Hadith Recall in Open LLMs

Dayam Nadeem, Safdar Mustafa, Ameen Ur Rehman, Shahab Saquib Sohail, Mohammad Nadeem, Usman Naseem

原文摘要

Large language models are increasingly consulted for religious knowledge, including the recall of specific Hadith (recorded sayings and actions of the Prophet Muhammad, peace be upon him). Unlike open-ended generation, Hadith recall is a high-stakes, verifiable task: a reference (collection, chapter, and Hadith number) has exactly one correct text, and any deviation from it constitutes a fabrication that can be mistaken for authentic scripture. This study introduces a bilingual (English/Arabic) benchmark that probes five open-weight, locally-deployable language models (Mistral-7B, Gemma3-4B, Jais-7B, ALLaM-7B, and Phi4-mini-3.8B) across all six canonical Sunni Hadith collections (Sahih al-Bukhari, Sahih Muslim, Sunan Abi Da'ud, Jami' al-Tirmidhi, Sunan an-Nasa'i, Sunan Ibn Majah), sampling 500 Hadith per collection (3,000 per model per language, 30,000 responses total). Each model is given only the bibliographic reference and asked to reproduce the exact Hadith text, with no retrieval augmentation, and is scored on lexical fidelity (BLEU) and semantic fidelity (multilingual cosine similarity), with refusals flagged separately. Across all five models and both languages, 0% of 30,000 responses achieve both high lexical and high semantic fidelity, i.e., an exact, verifiable match to the source text. Arabic responses fare uniformly worse than English. These findings suggest that current open-weight LLMs, used without retrieval grounding, are unreliable and potentially dangerous sources for exact religious-text recall, and that deployment for Islamic scholarship should mandate retrieval-augmented generation over a verified corpus rather than parametric memory. The complete code, prompts, evaluation pipeline, and scored outputs are publicly available at https://github.com/dayam8696/Hadith-Hallucination-Benchmark.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.41365

Generated storm lifecycles fill the rare-event tail of short rainfall records

Valerie Tsao, Nathaniel W. Chaney, Manolis Veveakis, Marta Zaniolo

原文摘要

Historically rare ''one-in-a-generation'' storms are occurring with unprecedented frequency, causing catastrophic damage and mounting casualties, and exposing the vulnerability of infrastructure designed around short observational records. To overcome the limitations of sparse data, we present StormGen, a generative machine-learning model that transforms an observed storm's initial hours into complete, physically plausible hourly rainfall lifecycles in minutes. Evaluated on a decade of southeast Texas rainfall, StormGen produced close counterparts for 4\5% of the most severe 2\% of storms--compared to just 3\% for stochastic storm transposition--and maintained spatial coherence over full 24-hour sequences. Crucially, from a single hour of initial rainfall, the model successfully reconstructed the specific lifecycle, spatial pattern, and extreme catchment totals of Hurricane Harvey itself, despite the event being entirely absent in training. Because storm timing alone can more than double peak flows, these complete generated scenarios provide a vital mechanism to stress-test flood protections against unprecedented extremes.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02585

From Language to Country to Valence: A Multilingual Audit of Stereotype Bias Across LLMs

Zihao Sun

原文摘要

Large language models (LLMs) are increasingly considered for education, recruitment, workplace decision support, and other consequential applications, making pre-deployment social-bias detection essential. Yet major benchmarks are English-centred or translated, focus mainly on negative stereotypes, and may miss linguistic and cultural variation. We investigate gender, racial, and religious bias using 12,000 locally written Reddit posts in English, Spanish, French, and German. Only search terms are translated, preserving local context. The dataset is balanced by language and category; English gender is additionally balanced by stereotype valence. Three open-weight and three proprietary API-based models are evaluated with a two-blank design that reduces grammatical-gender cues. Religion has the highest absolute choice imbalance in every model-language condition. Spanish is highest for gender, whereas German is highest for race and religion. The evaluated API models score below the open-weight models throughout. Negative English-gender items exceed positive items; Australian and Mexican contexts exceed their Canadian and Spanish counterparts. These descriptive differences measure choice concentration, not stereotype endorsement or causal country effects. The crossed patterns show how aggregate scores can conceal category- and context-specific behaviour, motivating closer inspection before deployment.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02583

BasisKV: Value Latents for Communication and Key Routing

Lixun Zhang, Zishan Shao, Kangning Cui, Yu Liang, Fan Yang, Yiran Chen

原文摘要

Long-context large language model (LLM) inference is increasingly constrained by the cost of maintaining an ever-growing KV-cache. Low-rank K/V projections provide a promising way to compress the KV-cache with minimal quality loss, but prior work has largely optimized KV-cache compression in isolation, leaving distributed inference, especially with tensor parallelism where attention computation is partitioned across multiple GPUs, largely underexplored. We make two observations: (i) tensor-parallel communication is an important cost when deploying low-rank KV-cache compression in distributed inference; and (ii) Value compression can be associated with the output projection, allowing the same low-rank Value representation to reduce both Value-cache width and tensor-parallel communication. Based on these observations, we propose BasisKV, a low-rank KV-cache compression method for communication-intensive, long-context LLM inference. BasisKV jointly optimizes the low-rank factors across per-GPU attention partitions against the complete attention output, rather than fitting each partition in isolation. The compressed Value cache is further reused to help identify which Key pages should be accessed: it predicts part of the Key information needed for routing, while a Key-specific residual stores what cannot be recovered from Value. The original Keys are then retrieved only for the selected pages to compute exact QK scores. Across multiple LLM families and system settings, BasisKV achieves a better quality--efficiency tradeoff than existing low-rank baselines, reaching up to 1.42x end-to-end speedup under 8-GPU tensor-parallel inference. Our code is available at https://github.com/alexz949/BasisKV

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02581

SMILE: A Structured Multi-Agent Clinical Reasoning for Liver Ultrasound Understanding

Haris Ghafoor, Minjee Seo, Seonaeng cho, Keungmo Yang, Jaejun Lee, Kyungho Yoon

原文摘要

Automated liver ultrasound report generation improves the diagnosis of liver diseases by producing clinically structured descriptions of anatomical and disease-related findings. However, current generative vision-language models (VLMs) often rely on a single reasoning pathway that maps ultrasound appearance directly to textual descriptions. In addition to the inherent vision-language modality gap, this design is limited for liver ultrasound because disease interpretation is constrained by subtle visual evidence, heterogeneous disease patterns, and limited paired supervision. To address these limitations, we propose SMILE, a Structured Multi-agent clInicaL rEasoning framework that uses multiple complementary specialist agents to interpret ultrasound images and consolidates their outputs through a judge-guided consensus mechanism. Our approach employs three specialized agents: an image interpretation agent for morphological descriptions, a clinical-stage reasoning agent, and a visual question answering (VQA) agent for targeted queries. These agent responses are then aggregated and scrutinized by a judge VLM using a consensus filtering prompt to produce a unified, clinically coherent output. Across a public liver ultrasound dataset and an internal clinical dataset, SMILE consistently outperforms state-of-the-art VLMs, achieving up to 19.0\% improvement in ROUGE-1 and 18.8\% improvement in clinical F1-score, while reaching VQA F1-scores of 0.82 and 0.65 on public and internal datasets, respectively. These results demonstrate the potential of SMILE for data-scarce and limited-supervision clinical settings. Our models, datasets, and code are available \href{https://github.com/CMME-Lab/SMILE}{here}.

cs.CV · cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02578

Probing Local Conditional Fit for Diffusion Model Membership Inference

ByungHee Cha, Naoki Murata, Yuhta Takida, Jong Chul Ye, Yuki Mitsufuji

原文摘要

Membership inference determines whether a candidate example was used to train a text-to-image diffusion model and is an important tool for auditing training-data use. We identify a directional membership signal in \emph{conditional prediction shifts}, the changes in predicted noise under alternative text conditions at a fixed noisy input: whether movement toward the alternative prediction initially lowers or raises the denoising loss from its value under the original caption. Members exhibit less room for such local improvement. To quantify this response, we propose \emph{LoFIT} (Local Conditional Fit), which measures the initial response of the denoising loss along normalized prediction shifts and aggregates it across multiple text conditions and diffusion states. LoFIT requires no target-model gradients and can be computed using only forward queries to the denoiser. Evaluations on four datasets show improved membership discrimination across fine-tuned and pretrained models. LoFIT distinguishes members early in fine-tuning, while the naive denoising-loss baseline remains near chance. Controlled comparisons show that the membership signal is concentrated in the first-order response, with little discrimination from prediction-shift magnitude alone.

cs.AI · cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02576

Beyond $L_\infty$-Norm Sets: Synthesizing Interpretable Non-Uniform Uncertainty Sets for Evaluation-Time State-Adversarial Robustness

Xulin Chen, Ruipeng Liu, Garrett E. Katz

原文摘要

Deep reinforcement learning (DRL) agents are vulnerable to perturbations of their observed state inputs. Prior works often constrain state adversaries with $L_p$-norm uncertainty sets and evaluate policy performance under the resulting perturbations. However, in continuous control tasks like robotics control, different state dimensions have distinct physical meanings and influence policy behavior to different degrees, which $L_p$-norm sets fail to capture or explain. Moreover, it is important to identify when and along which state dimensions a policy is vulnerable, independently of the algorithm used to train it. In this paper, we focus on evaluation-time attacks applied to pretrained policies. We propose \textbf{P}erformance-\textbf{D}egradation \textbf{N}on-\textbf{U}niform \textbf{U}ncertainty \textbf{S}et (\method{}), an interpretable formulation of uncertainty sets for state perturbations. \method{} is state-dependent, dimension-wise non-uniform, and can be synthesized online during policy evaluation. We first formulate an ideal constrained optimization problem for \method{} computation, then introduce a surrogate constraint to make the problem tractable. Experiments across multiple continuous-control environments, policy algorithms, and state attacks show that \method{} reveals variation in policy sensitivity across states and dimensions, while its performance degradation is moderately controlled.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02574

RLVR Sharpens the Pencil, Not the Mind

Shreeyam Bangera, Manya Gupta

原文摘要

Models like DeepSeek-R1 made Reinforcement Learning with Verifiable Rewards (RLVR) famous: reward a model for correct answers and it gets dramatically better at reasoning without being taught how. The leading explanation is sharpening---that the base model already knows how to solve these problems and RLVR just makes it more likely to reach for its best solution paths. This has only ever been checked by comparing before and after training, never by watching what changes inside across it. We tracked OLMo-2-1B across all 13 training checkpoints on GSM8K. First-try accuracy climbs from 58.0% to 61.3%, but ask the model 256 times and solution coverage actually rises from 94.5% to 96.9%, proving the knowledge was already there. We then planted reasoning mistakes into problems and trained simple probes to detect them. Those probes held between AUROC 0.798 and 0.840 across all 13 checkpoints and 3 error types, a spread smaller than the confidence interval; the model never builds a sharper internal picture of what a reasoning mistake looks like. The one place we find real internal change is at the final answer token, where a probe distinguishing early from late checkpoints hits an AUROC of 0.93, while the same probe on the actual reasoning steps lands near chance. RLVR makes the model lead with its best thinking. It does not change what that thinking is.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2609.24965

Jev for Scientific Decisions: Evaluating Semantic Choices and Their Consequences

Boyuan Deng, Shuyi Fan, Hongyang Zhang, Xinhong Xie

原文摘要

Scientific workflows often require choosing among known relations before a deterministic calculation can proceed. Whether observations share a culture, treatment or reference standard can change the scientific meaning of the resulting count or comparison. Once the evidence is prepared and the calculation is written as code, this choice reduces to one semantic judgment among listed candidates. We evaluate Jev on this step, using a harness that follows its documented guidance. The study compares twelve model configurations on twenty Choices in ten scientific cases built from published sources, each repeated five times. We measure semantic selections, downstream outputs and final claim labels separately. Jev tied with five other configurations at complete semantic correctness and achieved the lowest observed median latency and known mean cost among successful responses. On one question, whether samples shared a culture, three comparison models made seven wrong selections that changed downstream counts while preserving the correct final label. These results support using Jev to select relations when the evidence and candidates are prepared in advance, and show that evaluating it requires checking the relations and quantities that a workflow will reuse.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02571

Organizational Learning in Materials Discovery Campaigns: Which Databases, Calculations, and Agent Teams Pay Off

Kinston Ackölf, Taylor D. Sparks

原文摘要

More databases can make a materials campaign worse. Through an organizational lens, we trace each source's contribution. One band-gap campaign ranks seven anion families from archived measured gaps, one vote per source. In the first half of its budget, all seven pooled rank worse (rank agreement 0.737; 1 = measured order) than the best pool of three (0.851). As a stability check, choosing the pool on half the runs and testing on the rest kept the gap. Splitting Materials Project afterwards points to its transition-metal oxides and fluorides. Two databases from one pipeline add nothing over one, but pooling gives their shared ordering two votes. New calculations repay their cost early only when two or three campaigns reuse them. Flat language-model agent teams trailed a random-pick baseline (mean 0.876, sd 0.096, 20 searches). An adaptive Al-Mg-Si team, mostly interatomic potentials, was scored on unseen HSE06 calculations. In its first quarter of compute it found 49% of the ten lowest-formation-energy compounds, against at most 40% for fixed teams and 73% for MACE-MP-0 alone. Merging members whose orderings agreed carried most of its gain. This suggests pooling a few sources that fit the measurements, counting each pipeline once and charging for computation.

cs.MA · cond-mat.mtrl-sci · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02568

SeMo3D: Unifying 3D Generation, Editing, and Completion via Selective Modeling

Xiaokun Sun, Jian Yang, Zhenyu Zhang

原文摘要

3D generation, editing, and completion are typically treated as regenerating the complete 3D asset under task-specific constraints, even when only a local region needs modification. This leads to task isolation, inconsistency in content that should remain unchanged, and dependence on task-specific paired data. We address these issues with a key observation: the three tasks all synthesize a designated region while keeping the rest intact---the full region for generation, the target region for editing, and the missing region for completion. We term this paradigm Selective Modeling and build SeMo3D upon it; it naturally unifies the three tasks, preserves unchanged content by construction, and enables training without paired editing or completion data, since any 3D asset can be partitioned into a selected region and its context. For training, we propose region-selective denoising, which randomly selects regions of varying sizes on standard 3D assets and applies noise and loss only to them, jointly learning all three tasks. For architecture, we propose Omni3DiT, a multi-condition 3D diffusion transformer that distinguishes regions to create from those to retain and fuses image, text, and point guidance for fine-grained control. Experiments across three benchmarks show SeMo3D surpasses state-of-the-art methods on all tasks, delivering multimodal controllability in generation and strong consistency and accuracy in editing and completion. SeMo3D further serves as a unified modeling tool for agents, enabling efficient 3D creation through natural-language interaction. Project page: https://xiaokunsun.github.io/SeMo3D.github.io/.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.37682

Rank-Aware Differentially Private Release of Listwise Preferences for LLM Alignment

Junwei Chen, Manjiang Yu, Pengpeng Qiao, Yang Cao

原文摘要

Listwise preference feedback offers richer supervision for large language model (LLMs) alignment than pairwise comparisons, but a full ranking also reveals more sensitive user preferences. Existing privacy-preserving alignment methods focus mainly on pairwise feedback, while listwise alignment under local differential privacy (LDP) remains unexplored. Extending LDP to this listwise domain raises a critical challenge: the high sensitivity of the full ranking space demands excessive noise, neutralizing the benefits of listwise supervision. To address this challenge, we analyze the interaction between supervision granularity and privacy noise in downstream training, and introduce a rank-aware exponential mechanism that privatizes listwise preference data into a low-sensitivity granularity suitable for downstream alignment. The mechanism leverages ranking information to sample a fixed-size binary partition, concentrating the noise near borderline items instead of perturbing all items uniformly as randomized response (RR) does. Empirical evaluations on downstream LLM alignment tasks show that our mechanism consistently outperforms existing LDP baselines at matched privacy budgets. The code is available at https://github.com/tdsai-lab/RAEM.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2603.06082

Offline Materials Optimization with CliqueFlowmer

Jakub Grudzien Kuba, Benjamin Kurt Miller, Sergey Levine, Pieter Abbeel

原文摘要

Recent advances in deep learning inspired neural network-based approaches to computational materials discovery (CMD). A plethora of problems in this field involve finding materials that optimize a target property. Nevertheless, the increasingly popular generative modeling methods are ineffective at boldly exploring attractive regions of the materials space due to their maximum likelihood training. In this work, we offer an alternative CMD technique based on offline model-based optimization (MBO) that fuses direct optimization of a target material property into generation. To that end, we introduce a domain-specific model, dubbed CliqueFlowmer, that incorporates recent advances of clique-based MBO into transformer and flow generation. We validate this model's optimization abilities and show that materials it produces strongly outperform those from generative baselines. To support specialized materials discovery applications and broader interdisciplinary research, we release our code (https://github.com/znowu/CliqueFlowmer), model weights (https://huggingface.co/iamkuba/CliqueFlowmer), and an interactive demo (https://cliqueflowmer.znowu.workers.dev).

cs.AI · cs.CE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03747

Enhancing Stock Market Information Retrieval with Retrieval-Augmented Generation

Huan Phong Thai, Thu Anh Ngoc Le, Nhu Huynh Thi Le, Nhan Huu Tran, Lenh Cong Phan Pham, Hong Nhung Nguyen

原文摘要

Natural Language Processing (NLP) has been widely applied in financial domains, enabling automated analysis and retrieval of stock market information. However, traditional NLP-based systems often struggle with maintaining contextual accuracy and generating relevant responses in dynamic financial environments. To address these challenges, Retrieval-Augmented Generation (RAG) models have emerged as a powerful solution, combining retrieval-based methods with generative capabilities to enhance response quality. In this study, we present a Retrieval-Augmented Generation (RAG) system designed to generate responses related to stock market information. The system integrates retrieval-based and generative approaches to enhance the accuracy and relevance of financial text generation. To evaluate its effectiveness, we employ the ROUGE metric, specifically ROUGE-1, ROUGE-2, and ROUGE-L, to measure lexical and structural similarity between generated and reference texts. Experimental results on a dataset of 100 stock market-related queries demonstrate high ROUGE scores, indicating strong alignment with reference documents. Additionally, we assess latency to evaluate the system responsiveness, achieving an average response time of 1.16 seconds. These findings confirm that our RAG model is capable of providing accurate and timely stock market insights, making it a promising approach for financial information retrieval.

cs.IR · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.05688

Square-Root Regret for Adversarial Multiplayer Bandits without Collision Information or Shared Randomness

Chenyu Gan

原文摘要

We study cooperative multiplayer bandits with adversarial rewards and no collision information. In each of $T$ rounds, each of $m$ labeled players picks one of $K>m$ arms. A player alone on its arm receives that arm's reward in $[0,1]$; players who share an arm receive $0$. Each player observes only its own reward, so it cannot distinguish a collision from an arm with zero reward, and there is neither shared randomness nor a communication channel. Bubeck, Li, Peres and Sellke (2020) obtained regret $\widetilde O(KT^{3/4})$ for two players and $\widetilde O(mK^{3/2}T^{1-1/(2m)})$ for $m$ players, and conjectured that regret $Ω(T^{2/3})$ is unavoidable already for two players. We refute this conjecture. We give a strategy whose expected regret against every oblivious adversary is $O(m^{3/2}\sqrt{KT}\log^2(KmT))$, which is optimal in $K$ and $T$ up to logarithmic factors. Three ideas make this possible. (i) Bits are sent through deliberate collisions as random parity checks, and the regret spent while a message is delayed is charged to the number of arms with positive reward rather than to elapsed time: an adversary that jams the channel with zero rewards forfeits the regret it could inflict. (ii) An exponential-weights learner over sets of arms combines a frequently resampled coordinator arm with a follower assignment that changes only $O(\sqrt{mT/K})$ times in expectation; delayed updates are reconstructed exactly. (iii) We teach the followers a privately chosen row index in a fixed public table using reward observations alone, allowing them to coordinate without shared randomness. The bound holds simultaneously for all reward matrices, even ones chosen with knowledge of the table, and a random table works with probability $1-O(1/(KmT))$.

cs.LG · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02564

Central pivot point control of a body-attached spring-mass runner: periodic gaits, stabilization, template embedding and approximate analytic return maps

İzel Sever, O. Kaan Karagöz, M. Mert Ankaralı

原文摘要

The spring-loaded inverted pendulum (SLIP) describes the center-of-mass (COM) motion of runners but has no trunk. In trunk models, the hip torque couples posture and COM motion and destroys the central-force structure on which SLIP analysis relies. As a result, trunk models have been studied almost only numerically. We show that this structure can be restored. The central pivot point (CPP), a hip-torque law that directs the ground reaction force through the COM, decouples pitch exactly and reduces stance to a planar central-force problem. At upright posture, time-reversal symmetry produces one-parameter families of periodic gaits. A stance-phase pitch law and a stride-level regulator stabilize all computed gaits, also with bounded hip torque and friction. Under CPP, any radial force law in virtual-leg coordinates can be embedded independently of posture. For a linear radial template, the stance radius and the constant-momentum sweep have exact closed forms. With a new gravity correction, the resulting analytic return map, to our knowledge the first for a trunk model, predicts liftoff velocity and apex position with mean errors of 1.6% and 0.9%. The natural gaits of the embedded runner are symmetric and need no force step at touchdown. A near-reversibility argument explains their unit multiplier, and a scalar equation predicts them within 0.8%. A deadbeat stride controller based on the analytic map tracks changes of speed, height and posture. CPP thus makes trunk models accessible to the analytic tools developed for SLIP.

cs.RO · eess.SY · math.DS · physics.bio-ph

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.11213

AliO: Output Alignment Matters in Long-Term Time Series Forecasing

Kwangryeol Park, Jaeho Kim, Seulki Lee

原文摘要

Long-term Time Series Forecasting (LTSF) tasks, which leverage the current data sequence as input to predict the future sequence, have become increasingly crucial in real-world applications such as weather forecasting and planning of electricity consumption. However, state-of-the-art LTSF models often fail to achieve prediction output alignment for the same timestamps across lagged input sequences. Instead, these models exhibit low output alignment, resulting in fluctuation in prediction outputs for the same timestamps, undermining the model's reliability. To address this, we propose AliO (Align Outputs), a novel approach designed to improve the output alignment of LTSF models by reducing the discrepancies between prediction outputs for the same timestamps in both the time and frequency domains. To measure output alignment, we introduce a new metric, TAM (Time Alignment Metric), which quantifies the alignment between prediction outputs, whereas existing metrics such as MSE only capture the distance between prediction outputs and ground truths. Experimental results show that AliO effectively improves the output alignment, i.e., up to 58.2% in TAM, while maintaining or enhancing the forecasting performance (up to 27.5%). This improved output alignment increases the reliability of the LTSF models, making them more applicable in real-world scenarios.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.17262

SHARP-QoS: Sparsely-gated Hierarchical Adaptive Routing for Joint Prediction of QoS

Suraj Kumar, Arvind Kumar, Soumi Chattopadhyay

原文摘要

Dependable service-oriented computing relies on multiple Quality-of-Service (QoS) parameters to assess service performance. However, real-world QoS data are often sparse, noisy, and shaped by hierarchical dependencies arising from QoS interactions, geographical constraints, and network-level factors, making accurate prediction challenging. Existing deep learning-based methods often predict each QoS parameter separately, requiring multiple similar models, which increases computational overhead and limits their suitability for applications requiring joint decisions across multiple QoS parameters. Although recent joint QoS prediction studies employ shared architectures, differences in numerical ranges across QoS parameters can lead to task imbalance and negative transfer, while inadequate representation learning and suboptimal joint optimization can degrade prediction accuracy. We present SHARP-QoS, a joint QoS prediction framework with three components. First, hyperbolic graph and hypergraph convolutions in the Poincaré ball extract hierarchical features from QoS invocation and contextual graphs. Second, sparsely gated sub-network routing selectively shares contextual and cross-QoS features, and a gated fusion module combines them with the structural features. Third, an EMA-based loss-balancing strategy smooths short-term fluctuations in task losses to provide less fluctuating task weights. Experiments on three multi-QoS datasets covering two, three, and four QoS parameters show that SHARP-QoS achieves lower prediction errors than existing joint QoS prediction methods. Because its contextual branch relies only on public region and AS attributes, it supports prediction for new users and services. Ablation, hyperparameter, outlier, and scalability analyses further assess its performance, highlighting the trade-off between prediction accuracy and computational cost.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02561

EEFA: A Framework for the Ethical Evaluation of AI Agents

Swathi Pallikala, Yashas Hariprasad

原文摘要

The rapid deployment of autonomous AI agents across consequential domains including healthcare, legal services, nancial management, and enterprise workows has created an urgent governance challenge. While the eld of AI evaluation has produced sophisticated benchmarks for measuring agent capability, these frameworks measure exclusively what agents can do rather than how they do it. The ethical dimensions most critical to responsible agent deployment, in- cluding respect for human autonomy, harm avoidance, transparency and non-deception, privacy compliance, and accountability, are systematically absent from current evaluation practice. This paper addresses that gap through three contributions. First, a structured literature review of twelve leading AI agent benchmarks characterizes the ethical dimensions those bench- marks do and do not measure. Second, the Ethical Evaluation Framework for AI Agents (EEFA) is introduced as a comprehensive, operationalizable instrument for ethical agent evaluation, grounded in established AI ethics principles and designed for use by developers, auditors, and regulators. Third, a reproducible validation protocol is specied for applying the EEFA to deployed agent systems, with illustrative test scenarios across all ve framework dimensions. The EEFA is organized across ve primary dimensions: Autonomy Preservation, Harm Avoid- ance, Transparency and Non-Deception, Privacy Compliance, and Accountability and Traceabil- ity. The framework maps directly onto the requirements of the EU AI Act and the NIST AI Risk Management Framework, positioning it as a practical compliance assessment instrument for high-risk AI applications. Empirical application of the framework across diverse benchmarks and agent systems is identied as a priority direction for future work.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.15659

Harnessing Plastic Deformation for Continuous Passive Stability in Soft Grippers

Junlong Xiao, Yaoqiang Pan, Xuan Zhang, Michael Yu Wang, Chao Chen

原文摘要

Soft robots offer compliance and adaptability for interacting with complex environments, but their low structural stiffness can compromise stability under dynamic disturbances. Here, we present a continuously stable structure through plastic deformation (CSSPD), a hybrid metal-elastomer architecture that enables passive shape retention without continuous energy input. By deliberately exploiting plastic deformation in a kirigami-patterned metal layer, CSSPD establishes new equilibrium configurations while increasing structural stiffness and resistance to dynamic disturbances. A theoretical model is developed to characterize the deformation and stability of the hybrid structure and guide the geometric design of the metal layer. We further introduce a bioinspired paw pad that provides top-side support for enhanced grasp stability and serves as an integrated sensing interface for rapid object acquisition. A telescopic soft pressing mechanism (TSPM) mechanically couples paw-pad motion with tendon actuation. Experiments demonstrate passive holding forces of up to 16 N without continuous energy input, comparable to pneumatic actuation at 0.3 MPa, and stable grasping under pulsed accelerations of up to 400 m/s^2 when combined with pneumatic actuation. Energy-free perching on tree branches further demonstrates the potential of the proposed composite architecture for stable and energy-efficient manipulation.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.31028

Multimodal Progress-Residual Fusion for Anticipating Navigation Retreat in Robotic Flexible Endoscopy

Chi Kit Ng, Tianchun Wu, Jiachen Sun, Yuming Sun, Jianru Zhang, Hongliang Ren

原文摘要

Flexible robotic endoscopes provide access through tortuous anatomy, where friction, deformation, and wall interaction separate commanded insertion from useful distal progress. Operators interpret this mismatch and retreat to recover a navigable configuration. Recognizing the approach to that decision requires relating motor intent to distal response and the local visual scene. We introduce Multimodal Progress-Residual Fusion (MPRF), a causal model that anticipates operator retreat from commands, electromagnetic (EM) tracking, and endoscopic video. A nominal-progress model predicts the response expected in normal or successful navigation; a temporal predictor combines its EM residual with all three observed modalities. In a 150-episode colon-phantom collection, MPRF achieves 0.580 +/- 0.007 AUPRC across three seeds, compared with 0.511 for the strongest reduced-modality control. Complete multimodal histories provide the principal ranking advantage, while the residual adds an explicit intent-response representation with a small, uncertain increment over raw fusion. Analysis of frozen alarms relates warning timing to the behavioral label and explains the increase in event recall under a longer matching window. These results establish a basis for behavioral assistance during operator-controlled navigation.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02555

Sequential Recovery Updates for Learned Controllers under Successful-Trajectory Preservation

Zongtan Li, Tianming Liu, Ye Zhao

原文摘要

A competent learned controller already recovers from many disturbances. Improving it means learning a correction, and learning when not to apply it. Freezing a skill's parameters does not settle the second half. In a controller that routes among frozen skills, a new entry rule can redirect the states an old skill succeeded on. We therefore constrain entry instead of parameters. Each update searches for a feedback skill at a measured failure. It fits an entry rule that reads observations and rejects every state on an accumulated successful trajectory. The skill is admitted only if the composed controller reproduces the new recovery on its own and replays every protected trajectory exactly. A finite-trajectory condition states when this preserves the previous controller. One mechanism runs on four streams: two motions on each of two humanoid systems. Two updates per stream add 62 recoveries over 192 withheld perturbations, and lose none of the base controller's successes. We then price that guarantee. Removing the constraint recovers 33 more conditions but stops eight protected ones from completing at all, and excluding only what admission proves necessary recovers none of that cost.

cs.RO · cs.AI

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arXiv ↗PDF ↗