Artificial Intelligence
Automatically Building and Updating a Knowledge Graph of MLIP Models
Alexis Beer, Liudmyla Klochko and Mathieu d'Aquin
Complementing the many efforts in providing semantic representations of concepts, notions, and entities in materials science, we report and illustrate a process by which we can automatically build a knowledge graph of the fast evolving field of machine learning applied to the prediction of material properties, focusing on MLIP (Machine Learning Interatomic Potential). This LLM-based process relies on multiple steps, from information extraction in documents and articles to a validation loop using SHACL constraints to detect and correct errors. It is carried out on a model-by-model basis, focusing on the consistency of representation, therefore enabling an iterative construction where the addition of new models is facilitated. We illustrate the process by showing a few interesting aspects that can be queried from a knowledge graph built from the models listed in the Matbench Discovery leaderboard.
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Artificial Intelligence · Cross-listed from q-bio.QM
CircuitATLAS: Agentic reasoning over a systems neuroscience knowledge graph for target discovery in circuitopathies
Gabriel Ocana-Santero and Marko Tvrdic
Drug discovery for neurological disease has traditionally centered on the molecules altered by disease. But the molecules that cause pathology are not necessarily the best points from which to reverse it. Here, we ask which otherwise unaltered molecular control points can be engaged to restore pathological neural
circuits toward functional states. We present CircuitATLAS, a provenance-grounded systems-neuroscience knowledge graph and agentic framework for target discovery in circuitopathies. It structures literature-derived relationships across diseases, phenotypes, electrophysiology,
circuits,
brain regions,
cell types and molecular effectors, while deliberately excluding direct disease-gene and disease-
protein edges to reduce shortcut
reasoning. The graph contains 3.83 million nodes and 7.66 million edges, including 5.31 million LLM-extracted relations, and incorporates structured
datasets such as the Human
Cell Atlas and new
multimodal in vivo measurements. We then introduce an agentic workflow that reasons from measurable disease phenotypes through their
circuit and cellular substrates to molecular interventions, therapeutic feasibility and clinical constraints. Finally, we introduce a human-governed in vivo lab-in-the-loop linking hypothesis generation to experimental iteration. Within this framework an
agent nominated ATP1A3, the neuronal alpha3 Na+/K+-ATPase, as a control point on cortical excitability; interneuron-restricted expression of ATP1A3 abolished the beta- and gamma-band response to a focal 4-aminopyridine challenge in vivo, and the validated target was then carried into a structure-guided small-molecule campaign terminating in a defined assay to resolve the direction of modulation. CircuitATLAS thus provides a framework for discovering therapeutics based not only on what is molecularly disrupted in disease, but on what can be controlled to restore
circuit function.
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Artificial Intelligence · Cross-listed from cs.LG
Coding-Agent Benchmarks Should Match Their Users' Task Flows
Igor Slinko, Yaroslav Golubev and Sergey Titov
The evaluation of coding
agents generally strives to be as realistic as possible. In our study, we collect 4,782
agent sessions of real software engineers in JetBrains IDEs, which we call Production Sessions. Since our subject is interactive
agents, we study the sessions with at least three user messages (33% of the sample). These long sessions differ from issue-derived
benchmark tasks in two ways: (i) user requests span a far wider mix of task types - questions about the project's code, planning, review, refactoring, execution - and (ii) users switch between types throughout a session. Long-session samples from three public interaction corpora exhibit markedly different Task Flows (the distributions of session lengths, task types, and type-to-type transitions), so no single interaction distribution is universally realistic:
benchmarks should name a target use case and calibrate to measurements from it. We present SWE-TaskFlow, an approach for transforming any issue-derived
benchmark: it preserves the verified tasks and tests while steering the interaction toward a target Task Flow through
prompt splitting and verifiable repository QA, with a TaskFlow
Alignment Score (TFAS) for selecting among generated trajectories. In a pilot on 700 SWE-Bench Pro tasks, solving the task sequentially in several steps approximately doubles
agent cost without a stable change in resolve rate: the interaction protocol itself is an important dimension of evaluation.
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Artificial Intelligence
How Do Agentic LLMs Decide to Call Tools? A Tool-Call Vector Shaped by Suppression
Xijie Gong, Tingxu Han, Jiahao Zhang et al. (+5 more)
Tool calling, invoking external tools on demand, is central to agentic LLMs, yet the mechanism that decides whether a model calls a tool or responds directly remains poorly understood. Agentic
prompts are long and heavily scaffolded, combining role instructions, tool schemas, format templates, and the user's request across hundreds of
tokens, creating a noisy, highly entangled context in which no single controllable variable for mechanistic analysis is obvious. To obtain such a variable, we propose a method that converts complex agentic
prompts into minimal contrastive pairs in which a single request verb determines the tool-call decision: replacing an execution-verb (e.g., write) with an analysis-verb (e.g., discuss) reliably flips the decision, suggesting it is mediated by a compact internal state. We construct 500 such paired
prompts across Python, Java, and C++ (300 for mechanistic analysis, 200 held out for evaluation). We trace the decision to a vector, $\mu_\Delta$, that is both causally necessary and sufficient and generalizes beyond the discovery
prompts to native multi-turn $\tau^2$-Bench trajectories and verb-free requests. Behavioral ablations show that the scaffold establishes a tool-call prior; Transcoder decomposition then reveals that analysis verbs suppress this prior through features signaling that tool use is unnecessary, whereas execution verbs largely leave it intact. Downstream scaffold-reading
attention heads and MLP features read out the resulting state, and the same mechanism recurs across seven models from the Qwen, Mistral, and Granite families. Our code is available at https://github.com/XijieGo/MI4ToolCalling.
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All authors Xijie Gong, Tingxu Han, Jiahao Zhang, Wei Song, Ziqi Ding, Hanqi Yan, Youcheng Sun, Lijie Hu
Artificial Intelligence · Cross-listed from cs.CL
Which Language Should a Skeleton Speak? Language Choices in Multilingual Reasoning
HyeonSeok Lim, SeungWoo Song, Inho Won et al. (+3 more)
Skeleton-based
reasoning prompting is a promising training-free approach for structuring LLM
reasoning, but prior work largely assumes an English-centric setting. We propose the
Language-Aware Skeleton Exploration Framework (LASEF) to study skeleton-
language choice in multilingual mathematical
reasoning. Across math
benchmarks, model scales, and
languages, we show that English skeletons yield a small positive tendency on average, most visible for smaller models and low-resource
languages. However, few
language-level gains remain significant after correction, and English is not universally optimal. Combining greedy decoding, multi-rollout evaluation, translation ablation, and cross-
benchmark validation, we further find three patterns of skeleton-
language effects: directionally consistent, evaluation- and
benchmark-dependent, and asymmetric negative. These effects cannot be fully explained by generation quality alone. Overall, skeleton
language is a context-dependent design variable that requires multi-level exploration. All resources are released at https://github.com/lhsstn/LASEF.
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All authors HyeonSeok Lim, SeungWoo Song, Inho Won, Hoyun Song, Jihyo Kim, KyungTae Lim
Artificial Intelligence
SafeEvo: Deciphering the Safety Alignment Mechanism and Evolution in Language Models
Miao Yu, Hao Huang, Lu Yuan et al. (+3 more)
Safety interpretability advances the study of Large
Language Model (LLM)
alignment from behavioral constraints driven by data or
algorithms towards a deeper understanding of internal mechanisms. However, existing works have focused primarily on safety-related representations,
attention heads, or
neurons after
alignment, while largely overlooking the safety mechanisms in pretrained-only models and their
evolution across
alignment checkpoints. To address this, we propose SafeEvo, an interpretability framework from the
circuit (sparse subgraphs of an LLM) perspective. SafeEvo first applies an optimization-based extraction
algorithm to identify weak refusal
circuits in pretrained base LLMs that can independently express refusal behavior. Causally ablating these
circuits completely eliminates the base model's refusal of harmful inputs. SafeEvo then traces the
evolution of refusal
circuits across successive
alignment checkpoints and finds that their structures change progressively, suggesting that the
alignment tax may result from refusal-
circuit updates affecting utility-related parameters. To validate this, SafeEvo introduces Safety
Circuit Alignment (SCA), which confines safety updates to the refusal
circuits. Experiments across three LLMs and two
alignment algorithms show that, on average, SCA outperforms vanilla
alignment in three aspects: (1) stronger
alignment, lowering harmfulness score by 63.21%; (2) less over-refusal, yielding a 58.44% decrease in refusal rates for benign queries; and (3) better utility, retaining 99.58% of the original model capabilities.
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All authors Miao Yu, Hao Huang, Lu Yuan, Yunpeng Li, Kun Wang, Zuming Jiang
Artificial Intelligence · Cross-listed from cs.HC
Structured pre-generation elicitation versus single-shot prompting in AI-assisted enterprise decision-making: a randomised online experiment
William Scott-Jackson
Generative AI speeds, and mostly improves, professional work, but there is concern that users who delegate both the production and the evaluation of an answer may accept weak output and engage less with the underlying
reasoning (cognitive surrender). Interventions proposed so far, such as unassisted practice or slowing adoption, sit outside the working task. We tested a different approach: an interactive metacognitive scaffolding layer (Cognistance, a prototype developed at the Oxford Centre for Impact Research (OCIR) that asks users to clarify context, choose a strategic direction and explain their
reasoning before the AI generates a deliverable). Mean composite quality was 32% higher with the scaffold, with the same direction for every rater. Gains were largest for trade-off articulation and strategic coherence and absent for technical specificity. A large part of the aggregate effect reflected rescue of weak
prompts: floor-scored (off-task) deliverables fell from 34% to 5%. Among participants whose own
prompt already stated the data-localisation problem, the advantage was 21%. Treatment participants reported greater involvement and took about 2.4 minutes longer on average (10.46 minutes). Immediate recall scores were higher, which tentatively suggests better retention, but in this limited experiment, was not robust to sensitivity analyses. Self-ratings of quality did not track rated quality in either condition. Structured elicitation before generation improved the rated quality and task relevance of AI-assisted strategy documents at modest cost in time. Delayed retention, error detection and effects in live organisations are the priorities for the next stage of research.
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Artificial Intelligence
Learning Situation-Conditioned Thinking Policies for Long-Term LLM Agents
Hong Su
Long-running autonomous
agents must reuse accumulated
reasoning experience without allowing explicit historical
memory and LLM context to grow indefinitely. However, existing
memory mechanisms mainly retrieve, summarize, or compress past content and do not directly learn when particular kinds of thinking should be activated or discover new thinking knowledge from temporally dispersed experiences. This paper proposes a situation-conditioned thinking
memory framework that transforms historical
reasoning experience into a lightweight policy for predicting what should be thought about in the current situation, while leaving detailed
reasoning to a large
language model. Situations may represent temporal or spatiotemporal
evolution rather than only current states. Temporary experiences are also periodically analyzed across multiple independent episodes to identify repeated long-range regularities, which are consolidated into new thinking knowledge and further internalized by the lightweight policy. Experiments show that the learned policy achieves 1.000 F1 on temporal-rule generalization, improves DeepSeek
reasoning F1 from 0.789 to 0.868, reduces online processing time from 0.3636 ms to 0.0382 ms per query at 30,000 historical situations, and reaches 1.000 relation-discovery F1 and future-thinking accuracy after sufficient repeated cross-experience evidence.
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Artificial Intelligence
Dual- versus Single-Suggestion AI Support for Radiographic Interpretation in Residents: Randomized Multireader Study
Lin Wu, Zhe Xu, Hongyi Wang et al. (+10 more)
Purpose: To compare dual- and single-suggestion AI support for radiographic interpretation by residents, particularly when the shared AI suggestion was incorrect. Materials and Methods: This prospective, multicenter, randomized three-arm reader study was conducted at three hospitals in China from July to September 2026 (ChiCTR2600129243). After specialty stratification, 132 residents with fewer than 3 years of clinical experience were randomized 1:1:1 to GPT-5.4 alone (group A), GPT-5.4 plus Kimi-K2.6 (group B), or GPT-5.4 plus Gemini-3.6 Flash (group C); 123 were analyzed. Participants interpreted 60 radiographs before and after AI support. The primary outcome was accuracy change. Welch ANOVA and Holm-adjusted t tests compared support conditions; HC3 linear models assessed specialty interaction. Results: Among 123 residents (mean age, 24.1 years +/- 1.4; 65 women), radiology residents showed greater accuracy improvement with dual- than single-suggestion support (B-A, 6.69 percentage points [95% CI, 0.97-12.40]; C-A, 7.87 percentage points [95% CI, 1.64-14.11]; Holm-adjusted P = .030 for both), whereas accuracy change did not differ in non-radiology residents (P = .20). When GPT-5.4 was incorrect, AI-assisted accuracy was higher with dual- than single-suggestion support in radiology residents (40.1% and 40.4% vs 20.0%) and non-radiology residents (31.3% and 31.0% vs 12.1%) (all Holm-adjusted P < .001). The dual-suggestion effect differed by specialty (interaction difference, 10.44 percentage points; 95% CI, 4.36-16.52; P < .001). Conclusion: Dual-suggestion support may mitigate the influence of erroneous AI suggestions, with greater accuracy improvement observed in radiology but not non-radiology residents.
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All authors Lin Wu, Zhe Xu, Hongyi Wang, Feifei Zhou, Wei Deng, Chunlong Zhang, Yuting Zhu, Kaixiao Chen, Xiao Liang, Chen Yang, Yeyuan Chen, Hao Chen, Fuqing Zhou
Artificial Intelligence · Cross-listed from cs.RO
Adaptive Code Generation for Controlling Robots
Justus Flerlage, Thorsten Wittkopp, Alexander Acker et al. (+1 more)
Deploying
robots as Complex Adaptive Systems (CAS) in unknown and dynamic environments necessitates a transition from rigid command libraries toward intention-based autonomy, as natural
language represents the only medium capable of articulating complex goals beyond the capacity of finite instruction sets. While Large
Language Models (LLMs) offer a path toward natural
language goal description, their integration introduces significant challenges: the formalization gap between imprecise intentions and executable actions, the taxonomy gap induced by unpredictable environments, and the challenge of maintaining temporal state and progress awareness. This work introduces an architectural framework that enables robotic control by leveraging generative AI. The system follows a dual-AI design: an LLM translates high-level intentions into executable program code restricted to a formal robotic library and constrained by verifiable syntax, while a
Vision-
Language Model (VLM) provides semantic grounding via a
distillation process. To ensure robustness, the framework incorporates environment-driven replanning triggers based on
geometric and semantic thresholds, complemented by continuous runtime monitoring and an adaptive planning loop. Benchmarked across frontier models, our framework architecture demonstrates that grounding generative AI in a reactive, constrained loop enables robust fulfillment of complex intentions in dynamic and unknown environments.
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All authors Justus Flerlage, Thorsten Wittkopp, Alexander Acker, Odej Kao
Artificial Intelligence · Cross-listed from cs.LG
Collaborative Reasoning Distillation via Cross-Feedback and Coherent Curation
Taehoon Kim, Seunggeun Cho and Dongsu Han
Reasoning capabilities are critical for advancing Large
Language Models, yet current approaches either require massive computational budgets or struggle to effectively distill
reasoning to smaller models. Standard
distillation methods rely on outcome-based rewards, failing to distinguish between sound
reasoning and lucky guesses. We propose Collaborative
Reasoning Distillation (CRD), a framework that enhances
reasoning in compact models through three innovations: (1) interactive cross-feedback where teachers iteratively critique each other's
reasoning, (2) fine-grained step-wise quality assessment capturing logical validity independent of final answers, and (3) coherence-aware step stitching that synthesizes complementary strengths. Students are trained via
Reasoning Quality Optimization (RQO) with budget constraints. Our model, CRD-4B, achieves 97.3% on MATH-500 and 70.3% on AIME'25, surpassing baselines while using only 50K training examples, up to 12 times smaller than the
datasets of comparable models.
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Artificial Intelligence · Cross-listed from cs.CR
Correct Answers, Unsupported Findings: Evidence Binding in Forensic Reconstruction of LLM Agent Logs
Taehyeon Yun, Dongho Kim, Geonwoo Kim et al. (+3 more)
Forensic reconstruction of LLM-
agent actions requires not only recovering the correct value, but establishing which preserved record supports that finding. Tool logs, generated explanations, and local citation identifiers capture different parts of this evidence, yet a citation identifier does not establish a source unless its binding to a record is preserved. We audit this distinction using 64 mechanically checkable cases from saved AgentDojo Banking executions. Two LLM readers reconstruct source relationships under controlled variations in visible evidence and identifier-to-record bindings. We separately evaluate complete-record agreement, evidence-grounded findings, justified abstention, and unsupported assertions. With original identifiers and no binding table, Sonnet recovered every literal source location but made unsupported citation-source assertions in 26 of 28 cases requiring the missing relation; 22 nevertheless matched the complete reference. Adding explicit bindings improved grounded reconstruction for both readers, whereas identifier renaming alone provided no consistent remedy. A deterministic same-packet comparator correctly resolved the bounded task or abstained throughout. These results show that factual agreement alone is insufficient for evaluating forensic reconstruction of
agent logs and motivate preserving explicit record bindings to distinguish supported findings from correct guesses.
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All authors Taehyeon Yun, Dongho Kim, Geonwoo Kim, Juyoung Seo, Minseok Hur, Moohong Min
Artificial Intelligence · Cross-listed from cs.CL
How Do LLMs Change Predictions Under Negation?
Jongwook Yoon, Jongwon Lim, Sungjib Lim et al. (+2 more)
Negation is an essential feature of human
language, yet large
language models (LLMs) remain unreliable in processing it. We evaluate recent open-source and closed-source LLMs on our negation
benchmark and find that, in 37-71% of cases, they repeat the same answer under negation (e.g., "Madrid" for "What is not the capital of Spain?"). To understand and address this brittleness, we mechanistically examine how models operate under negation. Our main finding is that specialized
attention heads and MLP
neurons jointly implement negation by (1) suppressing
retrieval of the original answer (e.g., "Madrid") while (2) promoting a favored candidate within the answer category (e.g., "Paris"). This contrasts with accounts of human negation processing, in which information about the original answer helps to determine what should be excluded. Furthermore, we find that this difference from human processing is a key source of negation failures: the model's mechanism relies on suppressing the original answer rather than using it to determine what to exclude, so the model can repeat the original answer when suppression is too weak or when a bias toward particular answers prevents it from selecting an alternative. To address this weakness in the model's negation mechanism, we propose a training objective that requires larger shifts in answer preference for more confident original predictions, and show that it reduces negation failures with less degradation of general capabilities than standard fine-tuning baselines. Together, our results demonstrate how mechanistic analysis can reveal why a linguistic capability fails and guide training that targets the underlying limitation.
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All authors Jongwook Yoon, Jongwon Lim, Sungjib Lim, Woojin Cho, Yohan Jo
Artificial Intelligence
World Potential Model: Pretrained World Knowledge as Progress Potentials
Jun Zhao, Jixin Tang, Yang Shu et al. (+6 more)
Long-horizon
language agents often receive supervision only from terminal task outcomes, leaving little signal for distinguishing productive intermediate behavior from stagnation or even regression. Rather than learning a separate value function or process reward model for every task, we ask whether pretrained models can recognize task progress from their existing world knowledge. We formalize this capability with a World Potential Model (WPM), a goal-conditioned evaluator of task-relative realized progress in
agent contexts. In ALFWorld and ScienceWorld, off-the-shelf pretrained models substantially outperform chance at recovering realized-progress structure without task-specific evaluator fine-tuning. We further anchor these progress judgments to task-specific milestones to obtain scalar world potentials, whose temporal differences provide process-sensitive step-level credit for policy optimization. Under matched comparisons, WPM-guided optimization improves success over outcome-only GRPO across all evaluated configurations. Together, these results provide initial evidence that pretrained world knowledge can support reusable realized-progress evaluation and provide useful supervision for long-horizon
agents.
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All authors Jun Zhao, Jixin Tang, Yang Shu, Jinyang Wu, Yuyang Lu, Jingqi Tong, Hao Xu, Weifeng Ge, Qi Zhang
Artificial Intelligence
DrugTargetWorld: A Synthetic Biobank for Training and Benchmarking AI Scientists
Samuel Margolis, Paul Schmiedmayer, Alan Huang et al. (+12 more)
Drug target discovery requires distinguishing molecules that causally drive disease from those that are merely associated with it. Training and evaluating AI
agents to perform this workflow end-to-end is difficult because real world biobanks lack known causal ground truth and participant-level data is access controlled. We introduce DrugTargetWorld, a framework that procedurally generates simulated biobanks, or "worlds," with known but concealed causal structure. Each world contains genotypes,
proteins, health records, outcomes, and synthetic
magnetic resonance imaging (MRI) for 54,000 participants.
Agents must construct a disease phenotype, identify causal driver
proteins, infer the beneficial direction of modulation, and optionally conduct virtual 'wet lab' experiments. We evaluated nine
agents in 540 episodes across 20 cardiovascular worlds and three experimental budgets. Opus 5 and GPT-5.6 Sol achieved the highest mean composite scores, 39.98 and 35.38 of 100, respectively, and both recovered 64% of causal drivers on average. However, no
agent reliably distinguished misleading non-causal
proteins, and performance remained limited by the integrative judgments required to connect phenotype construction, causal evidence, and intervention decisions. By making each world's causal structure known to the evaluator but hidden from the
agent, DrugTargetWorld turns end-to-end drug target discovery into a scalable training and evaluation problem with verifiable reward.
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All authors Samuel Margolis, Paul Schmiedmayer, Alan Huang, Ethan Chen, Ishan Bhattacharjee, Atman Shah, Ben Viggiano, Fang Cao, Shriya Reddy, Roger Xia, Jack O'Sullivan, Daniel Katz, Matthew Wheeler, Euan Ashley, Bruna Gomes
Artificial Intelligence
Reliability of LLM Judges for Evaluating Entity Alignment
Vaibhava Lakshmi Ravideshik and Mayank Kejriwal
Entity
Alignment (EA) identifies equivalent entities across knowledge graphs and is critical for knowledge base integration and ontology merging. Evaluating EA systems at scale requires expensive expert annotation, making systematic assessment across diverse domains practically infeasible. LLM-as-judge evaluation offers a potentially scalable alternative, yet its reliability for structured prediction tasks like EA remains unstudied. We present the first systematic benchmarking study across three frontier models, three
datasets, and four EA systems, using perturbation bias diagnostics, meta-evaluation across all
dataset-judge-
prompt combinations, and counterfactual label-flip tests. We identify anchor bias, a failure mode in which judges invert discrimination when the system's decision label is visible. Label exposure causally collapses judge discrimination (J-ROC-AUC 0.12-0.87), while a label-free protocol recovers near-ceiling capability on distinctive-name
datasets (0.93-1.00) and significant recovery on biomedical pairs (0.93-0.95). Counterfactual experiments confirm causality (FSR 53-99%) and reveal a frontier model paradox: stronger judges exhibit greater label sensitivity, not less. A blinded two-annotator human evaluation (102 pairs, Cohen's kappa=0.902) confirms this mechanism directly. We release the first biomedical EA
benchmark (MeSH-SNOMED CT, 15K pairs) and a reproducible auditing framework for LLM judge reliability in EA. Code and data are available at https://github.com/vaibhavalakshmiravideshik/llm-as-a-judge-entity-
alignment.
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Artificial Intelligence · Cross-listed from cs.CR
MARS: Malware Analysis with Rule-Based Scoring of LLM Claims
Hyeongjun Choi
Large
language models can triage malware through direct verdicts or behavioral claims scored by an external policy. We present MARS, a malware triage framework, and compare direct classification with single-pass claim scoring using the same evidence collector and identical static evidence bundles for each model. The evaluation covers 1,195 PE and ELF binaries grouped into 1,001 near-duplicate clusters and six
language models, with deterministic rules providing a baseline. Direct classification is more accurate for all six models. On samples with usable outputs from both paths, its accuracy advantage ranges from 3.7 to 20.9 percentage points, with all 95% cluster-bootstrap confidence intervals for the differences above zero. It also achieves higher malicious alert recall in ten of twelve platform and model combinations. Claim mediation provides no consistent reduction in performance variation across models. Separate subset studies find more consistent alert decisions for direct classification and a larger recall loss for the claim path when predefined indicator fields are removed. In a family identification probe, claims yield higher accuracy than verdict labels but lower accuracy than evidence text. Retained claims expose the inputs to verdict computation and permit policy revision without another model call. We reproduce archived verdicts exactly and apply a revised policy to the same records, including outputs from two additional models withdrawn by their provider. Under the evaluated claim taxonomy and additive policy, these results favor direct classification when only a verdict is required, while demonstrating that retained claims support explicit policy inspection and revision.
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Artificial Intelligence · Cross-listed from cs.CV
WAPR: A Foundation Model for Wide-Angle Refinement in Unseen Object Pose Estimation
Yulin Wang, Mengting Hu, Hongli Li et al. (+2 more)
Real-world applications require 6D pose estimation to be accurate, fast, and scalable to unseen objects. This paper introduces WAPR, a zero-shot wide-angle pose refinement model that refines candidate poses with rotational deviations up to 90 degrees. With as few as 12 candidate poses per detected object instance, WAPR supports fast inference within 1 s per frame and reaches a pose-estimation throughput of up to 25 detected object instances per second. To support wide-angle training for rotationally symmetric objects, WAPR uses rotational
symmetry priors to canonicalize
symmetry-equivalent pose targets before loss computation. We further construct SA6D, a large-scale 6D training
dataset with such priors. SA6D obtains KASAL-assisted rotational
symmetry priors for 944 GSO scans and expands them through geometry and texture augmentation into about 50K augmented object instances and about 2M rendered RGB-D images. In addition, an angle-balanced loss stabilizes learning across different angular ranges by reducing the influence of uninformative large-error cases. Experiments on seven BOP core
datasets show that WAPR achieves state-of-the-art performance in unseen-object 6D pose localization and detection under both fast and unconstrained inference settings. Project page: https://github.com/WangYuLin-SEU/WAPR.
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All authors Yulin Wang, Mengting Hu, Hongli Li, Jianghao Zhou, Chen Luo
Artificial Intelligence · Cross-listed from cs.CV
KASALv2: Fully Automatic 3D Rotational Symmetry Classification and Axis Localization
Mengxin Zhang, Yulin Wang, Chen Luo et al. (+2 more)
Rotational
symmetry is an important prior in 6D pose estimation, improving pose accuracy and supporting
symmetry-aware evaluation. However, current
symmetry annotations for 3D objects remain largely manual or semi-automatic, often requiring predefined types or orders, which limits scalability. This work introduces a fully automatic, reference-free framework for
symmetry-type classification, rotational-order identification, and full-axis localization across all eight canonical 3D rotational
symmetry types. The method localizes a dominant high-order axis, infers its rotational order through self-consistency analysis, and reconstructs the complete
symmetry structure under a hierarchy-guided formulation. A texture-aware extension further models appearance-induced reductions in rotational order while preserving axis orientations. Experiments on idealized and real-world
datasets demonstrate strong accuracy and generalization, achieving 94.75% accuracy on 438 symmetric objects in GSO. Training FoundationPose with these priors improves accuracy by up to 0.9% across five BOP
datasets, showing that automatically estimated rotational priors improve downstream 6D pose estimation. Code is available at https://github.com/WangYuLin-SEU/KASAL.
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All authors Mengxin Zhang, Yulin Wang, Chen Luo, Yongzhe Li, Yijun Zhou
Artificial Intelligence · Cross-listed from cs.LG
Align Before You Combine: Reference Space Calibration for Supervision Without Ground Truth
Jackson Eshbaugh and Jorge Silveyra
We introduce a calibration-first framework that produces supervision scores without access to ground-truth labels or a shared annotation space. Our framework aligns subset-specific scorers using a synthetic ordinal reference space before fusion. This reference space is constructed from ordered calibration features that represent the latent concept, providing a common scale on which otherwise incomparable scorer outputs can be aligned. Because our calibration procedure uses the reference space rather than training samples, it is independent of the training set's empirical distribution. Across three
benchmark datasets, our framework consistently outperforms uncalibrated averaging and achieves higher primary-metric point estimates on the evaluation metrics than the best individual scorer. Performance relative to sample-dependent baselines varies by domain, with absolute differences below 0.02 on Ames Housing and below 0.01 on Breast Cancer Wisconsin and Wine Quality. After Bonferroni correction, differences remain significant for all three comparisons on Ames Housing and one on Breast Cancer Wisconsin. Additionally, we show that using fewer calibration levels per feature can closely approximate higher-resolution results at substantially lower computational cost. Together, these results support our framework as a viable approach to construct supervision scores when neither ground-truth labels nor a shared annotation space is available.
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Artificial Intelligence · Cross-listed from cs.RO
Not All Uncertainty Matters: Simulation-in-the-Loop Fast-Slow Reasoning for Decision-Critical Autonomous Driving System
Jiayi Chen, Shuai Wang, Guangxu Zhu et al. (+3 more)
Large
vision-
language models (VLMs) provide powerful open-world perception and
reasoning for autonomous driving, but their high computational cost and inference latency make continuous cloud-side use impractical. This motivates fast–slow collaboration, where efficient onboard modules handle real-time perception and control while cloud models provide high-level
reasoning only when needed. The key challenge is deciding when cloud
reasoning should influence time-critical driving decisions. Existing methods often rely on perception uncertainty, heuristic triggers, or resource-driven policies, without assessing whether resolving an uncertainty will improve planning. We propose SIGMA, a simulation-in-the-loop framework for task-oriented fast–slow collaboration. SIGMA embeds the planner into uncertainty assessment and evaluates how plausible scene realizations under semantic and
geometric uncertainty affect feasible trajectories and planning cost. Based on these outcomes, it estimates the expected reduction in planning cost from resolving uncertainty. We further introduce expected planning gain (EPG), a decision-level metric for cloud invocation, cloud-guidance integration, and request prioritization under deadline and resource constraints. Experiments in CARLA show that SIGMA reduces unnecessary cloud interactions while improving planning, efficiency, and navigation success in static and dynamic obstacle scenarios. Compared with fixed-period collaboration, SIGMA reduces unnecessary cloud interactions by 50%, improves navigation success by more than 6%, and cuts finish time by up to 26.2% in dynamic scenarios.
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All authors Jiayi Chen, Shuai Wang, Guangxu Zhu, Derrick Wing Kwan Ng, Chengzhong Xu, Kaibin Huang
Artificial Intelligence · Cross-listed from cs.CV
OmniCam: Omni-Camera Trajectory Generation via Geometry-Grounded Pose Token Learning
Zhenyang Liu, Chenjie Cao, Yisu Zhang et al. (+5 more)
Camera trajectories control viewpoint changes in
video generation, scene reconstruction, and robotic perception. Generating them from
language requires both scene geometry and target-aware framing. We introduce OmniCam, an autoregressive model that generates camera pose sequences from a single panorama and textual trajectory descriptions. Its geometry-grounded pose
token learning combines three components: a panoramic point-cloud encoder for omnidirectional
geometric context; hybrid absolute-rotation and relative-translation tokenization with temporally consistent quaternion signs; and separate
geometric and semantic conditioning streams with an explicit 3D target anchor. We also construct OmniCaT, containing 267,700 trajectories across four camera behaviors. On the reported OmniCaT evaluation, OmniCam reduces trajectory errors by 28–47% and collision rate by 65.8% relative to GenDoP retrained on OmniCaT. Against the best baseline for each metric, the ATE and collision reductions are 43.0% and 62.3%, respectively. Component ablations support the use of
geometric and target-aware conditioning, while downstream experiments examine camera-controlled
video generation and robotic active perception.
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All authors Zhenyang Liu, Chenjie Cao, Yisu Zhang, Xuhui Zuo, Xiangyang Xue, Yanwei Fu, Tengfei Wang, Chunchao Guo
Artificial Intelligence · Cross-listed from cs.LG
Safe on Average, Unsafe in the Tail: When Is the Episodic-Cost Tail Controllable?
Samuel Tetteh and Cody Fleming
Safe reinforcement learning seeks policies that maximize return while satisfying constraints on cumulative cost. Most methods impose these constraints on expected episodic cost. Consequently, standard evaluations report mean episodic cost without characterizing how cost is distributed across episodes. A policy that satisfies the mean-cost criterion may therefore remain unsafe in its worst episodes. Mean-cost reporting neither identifies this tail violation nor shows whether it can be brought within budget while preserving return. In this work, we measure the episodic-cost tail using $\mathrm{CVaR}_{0.1}$, the average cost of the worst $10\%$ of episodes. We classify a policy as tail-safe when $\mathrm{CVaR}_{0.1}$ is within the safety budget. This allows us first to identify policies that are safe on average but unsafe in the tail and then to study whether their tail violations can be controlled while preserving return. To identify tail-unsafe policies, we evaluate five standard
algorithms on three Safety-Gymnasium navigation tasks. We then examine four constraint families on dense-hazard navigation and assess tail control across four navigation and four locomotion tasks.
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Artificial Intelligence · Cross-listed from cs.CV
SpatialUQ: Post-Hoc Uncertainty Quantification from Spatial Consistency in Black-Box Vision Models
Md Kawsher Mahbub, Milon Biswas, Mirza Niaz Morshed et al. (+1 more)
Clinical
vision models are often deployed as frozen black boxes with no access to internals, retraining, or ground truth at inference time. We introduce SpatialUQ, a post-hoc uncertainty method using only output probabilities. It measures the Jensen-Shannon divergence between the global prediction and the mean of five fixed spatial crops in six deterministic forward passes. The premise is simple, trustworthy predictions are spatially consistent. On NIH ChestX-ray14 (DenseNet-121, $N{=}25{,}596$), our Multicrop Uncertainty Score (MUS) reaches $0.784$ failure-detection AUC versus $0.664$ for MC-Dropout ($p{<}10^{-6}$) at one-fifth the compute, with native calibration ($\text{SCE}{=}0.049$ vs.\ $0.127$ for $\ell_1$), the best-calibrated among methods above 0.78 AUC. A supervised fusion of MUS with
entropy, confidence, and $\ell_1$ reaches $0.832$, outperforming a five-member ensemble ($0.813$). MUS scales with model quality, reaching $0.899$ with BiomedCLIP ($\rho = 0.846$), while this relationship remains meaningful in-distribution ($\rho = 0.523$) but breaks down under severe distribution shift (VinBigData, $\rho = 0.027$). MUS is well-suited to diffuse findings but is less dependable for small focal lesions such as nodules. Code and experimental materials are publicly available at https://huggingface.co/
datasets/kawsher11/SpatialUQ.
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All authors Md Kawsher Mahbub, Milon Biswas, Mirza Niaz Morshed, Wei Yu
Artificial Intelligence
Ream: Unfolding Mutual Awareness in Human-Agent Workspaces
Peiling Jiang, Sangho Suh, Varsha Kishore et al. (+6 more)
As AI
agents work alongside humans in shared workspaces, a mutual awareness challenge arises:
agents act at speeds that outpace human monitoring, and users' evolving interests are not always expressed in chat. This challenge is especially pressing in literature review, where both parties retrieve, read, and synthesize a growing body of papers. We present Ream, a literature review workspace that supports mutual awareness through structured artifacts, bidirectional engagement tracking, and localized visualizations. Users can see each party's activity within these documents, and
agents can retrieve the same history to guide their work. In studies with eighteen researchers, participants used these traces to inspect evidence, steer
agents, communicate through annotations, and reflect on their research focus. Shared histories also helped
agents build on earlier work. These findings inform how engagement traces within shared documents can support transparency, personalized assistance, and coordination in human-
agent knowledge work.
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All authors Peiling Jiang, Sangho Suh, Varsha Kishore, Jonathan Bragg, Haijun Xia, Pao Siangliulue, Daniel S. Weld, Amy X. Zhang, Joseph Chee Chang
Artificial Intelligence · Cross-listed from cs.RO
Sparse Feature Policy Unlearning Mitigates State Hallucination in Vision-Language-Action Models
Jiho Lee, Jeongeun Park, Heayoun Choi et al. (+2 more)
Vision-
Language-Action (VLA) models have shown strong generalization in robotic manipulation by leveraging rich representations from pretrained
vision-
language models. However, their deployment in real-world environments remains limited by recurring unreliable behaviors. In this work, we study state
hallucination, a recurring failure pattern in which a VLA continues acting as if an unrealized
robot-object state had been achieved. Our analyses find that state
hallucination coincides with weakened
attention to task-relevant visual regions, and a mechanistic interpretation via sparse autoencoders reveals that
hallucination-associated sparse features are activated when these failures occur. Based on this analysis, we propose SOUL (Sparse feature pOlicy UnLearning), which selectively unlearns policy knowledge associated with state
hallucination behaviors, where sparse features identified from
hallucination failures and successful behaviors serve as explicit forgetting and retention targets, respectively. Experiments across VLA architectures in simulated and real-world environments show that our method substantially reduces hallucinated failures and improves task success without substantially compromising the existing manipulation capabilities. These results suggest that interpretable feature analysis provides a practical basis for selectively modifying undesirable knowledge in
robot policies.
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All authors Jiho Lee, Jeongeun Park, Heayoun Choi, Taekyung Kim, Eunwoo Kim
Artificial Intelligence
Cognitive Schemas, Laws and Tasks
Antal Jakovác and András Telcs
This paper asks how explicit representations can support reusable cognitive schemas in knowledge-based problem solving. We develop a structural framework in which schemas are organized by the information and relations required for their use, rather than introduced as unrelated primitives. The framework also distinguishes context-dependent relations from more stable structures that can be reused across different representations. Tasks are described through the information available, the unknowns to be determined, and the constraints that admissible solutions must satisfy. This makes it possible to separate limitations of the representation from limitations of the solving procedure. In particular, we distinguish inconsistency, underdetermination, and contextual insufficiency, where the current representation lacks distinctions or relations required by the external task meaning. We also show formally when a reduction of representation preserves the task-relevant solution structure. The resulting task–schema interface offers a structured way to describe representational conditions relevant to problem solving. It supports the reuse and stabilization of derived knowledge while remaining independent of the particular mechanism used to generate candidate solutions. This may provide a useful component for future solver architectures that combine structured knowledge, verification, and learned proposal mechanisms.
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Artificial Intelligence
The Attribution Blind Spot: Layerwise Trajectory Diagnostics for Source Reliance in Retrieval-Augmented Language Models
Zhe Yu, Wenpeng Xing, Yunzhao Wei et al. (+4 more)
A
retrieval-augmented model can match a document without relying on it. Controlled knowledge conflicts make source choice observable and let us ask a second question that prediction alone cannot answer: which internal-state properties define useful intervention directions? We study paired hidden-state changes with Latent Trajectory Shift (LTS), a signed projection onto a training-fitted first principal component (PC1), and keep verified training exposure separate from behavioral source choice. Across the evaluated conflicts, state-change magnitude is often the stronger predictor, whereas signed PC1 is the stronger selective controller: equal-norm interventions change source preference while better preserving non-target behavior, and the frozen direction transfers across the tested
datasets and aligned model pairs. A same-system OLMo study further combines positive choice and control results with inconclusive exposure detection at the achieved power. The central result is a separation: representations that diagnose what a model will choose need not be the representations that best control that choice.
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All authors Zhe Yu, Wenpeng Xing, Yunzhao Wei, Bo Yang, Chen Ye, Gaolei Li, Meng Han
Artificial Intelligence · Cross-listed from cs.DB
Correspondences as Decisions: JevNexus for Decision-Centric Schema Matching
Runze Li, Hanchen Wang, Ying Zhang et al. (+1 more)
Schema matching increasingly uses generative
language models to rerank retrieved column candidates, although the underlying task is a bounded correspondence decision. We present JevNexus, which combines typed pairwise decisions with schema/instance evidence and invokes listwise refinement only when the evidence disagrees and the fused margin is small. The evaluation covers 561 cases from six
benchmark families. JevNexus obtains
dataset-macro MRR and Hits@1 of 0.930 and 0.909, compared with 0.926 and 0.903 for Magneto, while reducing mean latency from 123.452 to 15.929 seconds (7.750). Paired analysis finds no statistically significant difference in either MRR or Hits@1. The gate invokes listwise refinement for only 5.665% of source columns and avoids the degradation caused by unconditional refinement. Code and experimental artifacts are available at https://github.com/RazeenLI/JevNexus.
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All authors Runze Li, Hanchen Wang, Ying Zhang, Wenjie Zhang
Artificial Intelligence
MIMESIS: Learning User Simulators as Training Environments for Interactive Agents
Hoang Phan, Dat Huynh, Andrey Zhmoginov et al. (+7 more)
Training and evaluating interactive
language agents typically requires rich user interactions, yet collecting human feedback is expensive and difficult to scale. Simulated users offer a scalable alternative, but they must both resemble real user behavior and provide useful learning experiences for
agents. In contrast, most
agent-training frameworks rely on off-the-shelf assistant LLMs, whose helpfulness can make them overly cooperative, explicit, and behaviorally homogeneous compared with real users. We introduce MIMESIS, a purpose-built user simulator trained on human conversations with explicit
reasoning supervision and 13 realistic behavioral patterns derived from real user interactions. Empirically, our 9B model achieves a SOUL-Index of 65.7, surpassing the strongest frontier model. Compared with Claude-Opus-5, the strongest baseline on RealUserSim and SimulatorArena, MIMESIS improves behavioral fidelity by 13.4 points and reduces Turing distance by 3.6 points, respectively. We then freeze the simulator and train an
agent by interacting with the frozen simulator using multi-turn reinforcement learning. Across eight environments, training with MIMESIS yields better
agent performance than training with GPT-5.5 under all nine unseen user simulators, demonstrating stronger generalization to new user simulators. Moreover, we propose Coached On-Policy Self-
Distillation (CSD), which leverages simulator-generated private
reasoning traces and subsequent utterances as feedback on how well the
agent addresses user needs. A coach converts this information into concise coaching notes that describe how the
agent can better anticipate user needs and adapt its behavior over the course of an interaction. CSD turns this feedback into dense,
token-level supervision beyond sparse task rewards, yielding further gains across all nine evaluation user models.
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All authors Hoang Phan, Dat Huynh, Andrey Zhmoginov, Qi Zeng, Wancen Mu, Yue Cao, Shengjie Bi, Yun He, Changdae Oh, Deren Lei