Knowledge
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Bootstrapped On-Policy Distillation
2026-11-10 07:30:34Yue Wu, Xueyu Zhou, Yangrong Hu, Jian HuangAbstract
On-policy distillation (OPD) improves language models by distilling teacher predictions on student-generated trajectories, while on-policy self-distillation (OPSD) removes the need for an external teacher by using a reference-conditioned copy of the model as the teacher. Despite its great early improvements, we find that OPSD does not remain consistently effective under continued training and can eventually suffer substantial performance degradation. We study this training behavior and link the degradation to a growing mismatch between the evolving reference-free student and the frozen privileged teacher. In the early stage of OPSD, the privileged teacher remains closely aligned with the student and provides useful guidance for improving the student's generation trajectories. However, as training progresses, the growing distributional mismatch between the privileged teacher and reference-free student can cause the teacher to provide misleading supervision, ultimately degrading student performance. This phenomenon motivates us to first leverage privileged reference supervision to acquire useful knowledge and then transfer it to a fresh student. We introduce Bootstrapped On-Policy Distillation (BOPD), a two-stage framework that first uses OPSD to internalize task-relevant information from reference-conditioned supervision into an intermediate model, and then distills this reference-free intermediate model into a fresh copy of the initial model. Experiments across models show that BOPD improves mathematical reasoning and code generation beyond both the initial models and the constructed OPSD teachers. These results suggest that privileged reference supervision can be more effectively exploited by first transferring its task-relevant signal into a task-adapted model and then distilling from that model without reference conditioning.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13680
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DexForesight: Grounding Dexterous Policies with Implicit Spatial Foresight
2026-11-10 07:28:24Zhongxi Chen, Yi Yang, Xinyi Chen, Mingzhe Ye, Yaping Li, Zhihong Liu, Congsheng Xu, Haonan Wang, Zhijie DengAbstract
Dexterous manipulation requires rich spatial foresight over both the surrounding scene and the robot hand. Existing world-action models often acquire such predictive knowledge through explicit future prediction. We introduce DexForesight, a framework that learns spatial foresight implicitly by injecting dynamics-aware world representations into the latent space of a dexterous policy. During training, DexForesight learns an action-conditioned spatial world model from offline scene depth, kinematic hand geometry, and demonstration trajectories. The world model predicts 3D hand-point motion and visual feature changes, capturing both articulated hand dynamics and scene changes under demonstrated actions, including the motion of fingers occluded from the cameras. We then freeze the world model and align policy queries with its predictive world queries, transferring the learned spatial dynamics into policy latents and using them to condition action generation. At deployment, only the policy is retained, operating on RGB observations, language, and robot state without depth input or explicit future prediction. On 11 DexJoCo simulation tasks, DexForesight improves the overall success rate of the base policy from 49.4% to 60.5%. On three real-robot dexterous manipulation tasks, DexForesight achieves 58.3% success, compared with 50.0% for the strongest baseline. Project page this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2611.11166
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Benchmarking LLM Next-Step Recommendations with Knowledge Space Theory
2026-11-10 07:15:25Peter Steiner, Jan HochweberAbstract
Selecting an LLM for next-step recommendation requires benchmarks that reflect the intended educational task and domain. We present a benchmarking method that uses Knowledge Space Theory (KST) as a formal evaluation reference. Given a task catalogue and an existing prerequisite relation, the method derives admissible knowledge states and the complete set of immediate next steps for each state. Recommendations generated by a model under a specified prompt are compared with these reference sets. A sample evaluation study uses 2,400 responses from six models across four mathematics domains, four prompt conditions, and two catalogue orders. Exact-set accuracy was 61.3% with descriptions only, 62.8% when models were asked to reconstruct prerequisites, 96.3% with supplied prerequisite relations, and 98.2% with the complete knowledge space. Performance varied across models and domains, and recommendation sets agreed across the two presentations in 85.25% of pairs. The benchmark supports comparisons of model-prompt combinations against a context-specific reference. Its scores describe structural recommendation accuracy under the evaluated conditions; they do not establish learning benefit or the necessity of using an LLM for selection.
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URL
https://arxiv.org/abs/2611.13667
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MNN-Pocket-0.3B Technical Report
2026-11-10 06:53:47MNN TeamAbstract
This technical report presents MNN-Pocket-0.3B, a compact 300M-parameter dense language model designed for efficient on-device deployment and supporting context lengths of up to 32K tokens. With an INT4 deployment footprint of only 173 MB and decoding throughput of up to 466 tokens/s on iPad Pro M5 (Metal), it can be seamlessly integrated into applications on edge devices such as smartphones and smart glasses. MNN-Pocket-0.3B is developed through the co-design of its architecture, data, training pipeline, and inference system. Hardware-aware architecture selection and dependency-aware vocabulary pruning balance model capability with on-device memory and compute constraints. A four-stage pre-training pipeline covering approximately 4.1 trillion high-quality Chinese and English tokens equips the model with broad knowledge and reasoning capabilities. This is followed by a three-stage post-training pipeline comprising instruction fine-tuning, multi-teacher on-policy distillation, and quantization-aware training. Finally, hardware-specific optimizations in the MNN inference engine enable fast, memory-efficient inference and long-context processing. Extensive evaluations demonstrate that MNN-Pocket-0.3B achieves frontier-level performance among models of comparable size, with particularly strong results in Chinese and English knowledge and reasoning, mathematics, coding, and instruction following. It consistently outperforms similarly sized open-source models and even surpasses several 0.5B-parameter models on instruction-following benchmarks. We release MNN-Pocket-0.3B with open weights and ready-to-use MNN deployment packages. We hope it will provide a practical foundation for capable, responsive, and memory-efficient edge applications while advancing research and development in on-device language models.
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URL
https://arxiv.org/abs/2611.12311
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Megrez: A Knowledge-Guided Agent for Formal Mathematics over MK Set Theory
2026-11-10 05:42:16Tiancheng Xu, Wensheng YuAbstract
General-purpose coding agents equipped with proof-assistant tools have shown strong performance on standalone competition problems, yet cumulative textbook formalization inside a low-resource mathematical library still faces three obstacles: aligning textbook concepts with library declarations, keeping long-horizon search synchronized with live proof states, and integrating locally verified proofs reliably into a growing codebase. We present Megrez, a knowledge-guided formalization agent for Rocq libraries built on Morse--Kelley (MK) set theory. Megrez coordinates a general coding agent through three mechanisms: (1) a source-rebuildable knowledge graph merging formal declarations, compiler references, and curated metadata to support retrieval via Chinese aliases, concept clusters, proof dependencies, and multi-lane full-text ranking; (2) a feedback-driven 1--5 tactic short-burst proving loop paired with a goal-local branch ledger and exponential-interval structured reflection; and (3) a three-check acceptance pipeline (statement fidelity, axiom auditing, and full builds) coupled with a multi-corpus Provider stack and a candidate-bound recoverable batch state machine. Deployment and evaluation on a point-set topology corpus demonstrate effective cross-chapter formalization and scalable premise retrieval.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13613
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One Byte, One Token: Byteifying a 27B Hybrid-Attention LLM with Self-Generated Data
Abstract
Byte-level BPE fragments Chinese text (for Qwen3.8 on Chinese Wikipedia, 3.84 bytes per token on average; 64% of tokens are single characters) and hides spelling from the model. We ask whether an already-trained 27B subword model can be converted directly into a pure byte-level model (one byte, one token), without a patching structure or a local encoder, while keeping most of its ability. Starting from Qwen3.8-27B (48 Gated DeltaNet layers + 16 full-attention layers), we shrink the vocabulary from 248,320 to 384 entries (the 256 byte symbols already in the BPE vocabulary plus special ids), leave every other weight untouched, and continue training in a two-stage curriculum (encoding first, then reading) on 4.77 GB of supervised bytes: all dialogue/behavioral supervision is generated on-policy by the original model itself (character dictionary, word dictionary, general dialogue, coding sessions), and knowledge reading uses encyclopedia text (54% of supervised bytes). Training takes 178.6 h on 8xH200. On the same GPU and serving stack, the byte model's gap to the original falls within the paired bootstrap interval on MATH-500, HumanEval+, MMLU-Pro and non-thinking GSM8K, and it loses 4-6 points on C-Eval, thinking-mode GSM8K and non-thinking IFEval. Without thinking it leads on every character-level task (CUTE 75.2 -> 84.0, EXECUTE-zho 81.6 -> 93.9, our Chinese character suite 54.4 -> 64.0), but lookup knowledge about Chinese characters such as pronunciation and stroke count drops, and thinking does not recover it. The cost is throughput: per-step speed is identical, but each step yields 3-5x less output; Chinese characters per second is about 1/4 of the original, and prefill at 64K bytes is 6.5x slower. We also document four byte-model failure modes (greedy runaway, sampling breaking UTF-8, ineffective penalty terms on a small vocabulary, repetition) and their fixes.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13607
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An evidence-traceable benchmark of clinical reasoning in large language models
2026-11-10 04:28:57Zhiqun Zhao (Advanced Medical Research Institute and Key Laboratory for Experimental Teratology of the Ministry of Education, Cheeloo College of Medicine, Shandong University, Jinan, China), Xiaodong Yang (Department of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China), Bruce Gu (Shandong Computer Science Center, National Supercomputer Center in Jinan, Jinan, China, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China), Ming Li (Department of Rheumatology and Immunology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China), Yi Fu (Information Network Management Office, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China), Song Li (Department of Medical Oncology, Qilu Hospital, Shandong University, Jinan, China), Qi Mei (Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China), Zhi Liu (Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China), Kai Zhang (Department of Breast Surgery, General Surgery, Qilu Hospital, Shandong University, Jinan, China), Kexue Fu (Shandong Computer Science Center, National Supercomputer Center in Jinan, Jinan, China, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China), Jing Wu (Department of Central Laboratory, Public Health Clinical Center Affiliated to Shandong University, Shandong University, Jinan, China), Xiaofei Lu (Department of Breast Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, China), Yule Li (Advanced Medical Research Institute and Key Laboratory for Experimental Teratology of the Ministry of Education, Cheeloo College of Medicine, Shandong University, Jinan, China), Zheng Wang (Department of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China), Haiquan Lu (Advanced Medical Research Institute and Key Laboratory for Experimental Teratology of the Ministry of Education, Cheeloo College of Medicine, Shandong University, Jinan, China), Xiaoming Wu (Shandong Computer Science Center, National Supercomputer Center in Jinan, Jinan, China, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China)Abstract
Benchmark scores for medical large language models (LLMs) are difficult to interpret when scoring criteria cannot be traced to clinical evidence. Here we developed Med Evidence Bench (MEB; this https URL), comprising 1,536 physician-constructed items across three levels: guideline knowledge, standardized clinical scenarios and real-world laboratory scenarios. Each criterion stating a medical claim is linked to a versioned source-text span. Across ten LLMs, the Evidence Trace Score (ETS) was 0.975-0.997 for guideline knowledge, 0.292-0.658 for standardized scenarios and 0.182-0.447 for real-world scenarios, and rankings varied across levels. For real-world scenarios, scores for identifying clinical information (mean, 0.770) exceeded those for matching evidence (0.165) or formulating recommendations (0.160). On 1,296 criterion-level judgments, automated grading agreed with the majority vote of three physicians (macro-F1, 0.930; 95% confidence interval, 0.915-0.945), although agreement was lower for evidence application. These findings from Chinese-language materials indicate that guideline recall does not ensure patient-specific evidence use and do not establish clinical effectiveness.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13562
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Key Institutional Considerations for Designing Effective Independent Verification Organizations
2026-11-10 04:11:48Uma Kalkar, Rumman Chowdhury, Reva Schwartz, Gabriella WatersAbstract
Independent verification organizations (IVOs) have emerged as a popular, bipartisan solution for facilitating high-quality independent evaluations of AI technologies. But momentum is outpacing design: there is emerging consensus on the necessary operating conditions for robust IVOs, but little discourse on institutional and policy specifications to ensure those conditions are met. IVOs are currently envisioned primarily as a mechanism operating at the AI frontier and model level to evaluate catastrophic risks. Little work has yet been done on how to establish or enforce an IVO ecosystem that is equipped to handle myriad risks (i.e., beyond catastrophic ones) and operate at the system and application level across the deployment and use stages of the AI lifecycle. This paper argues that IVO design should ensure the capacity for evaluating AI models, systems, and applications in context for both frontier companies and enterprise companies & AI-adopting organizations. Additionally, this paper provides early considerations to advance IVO discourse from normative to operative, and from existential to everyday risks. Drawing from existing institutional constructs in financial services, education, public health, and science, we outline key considerations on whom IVOs serve, what access they require, how they interact with external governing structures, how they are funded, and how they fit into the wider assurance ecosystem. Much of the structure and knowledge necessary to holistically design IVOs and their ecosystem does not yet exist, even as companies rush to ink agreements with their chosen evaluators.
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URL
https://arxiv.org/abs/2611.10168
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EJAI: Judgment-Preserving Civic AI for Data Center Environmental Justice
2026-11-10 03:50:04Evan Pa\v{c}es-Wiles, Bryen Alvarez, Sara Gulabani, Akshara Kolipaka, Snehalkumar 'Neil' S. GaikwadAbstract
Communities in the United States and around the world are contesting data centers, the physical infrastructure of AI. These disputes pose environmental justice issues: residents must weigh unevenly distributed burdens using evidence scattered across metrics, corporate claims, policy documents, and public testimony. A formative study with 10 users showed that dashboards and single-agent AI can settle questions before people examine that evidence. Here we present EJAI, a source-grounded system that links an interactive map, retrieved evidence, missing-data statements, and two stakeholder personas that allow people to inspect evidence and form their own policy recommendations. In a small functional benchmark of 21 queries, retrieved evidence supported 98.4% of factual claims and the system acknowledged missing evidence in 32 of 33 cases. Persona alignment carried a grounding cost: the Corporate Executive omitted some evidence that conflicted with its assigned priorities. In a within-subjects study, 15 participants acting as residents prepared for a simulated town hall. Some checked cited AI claims while treating map metrics as official, some made conditional recommendations with safeguards such as water-use disclosure, and some found the personas simplified or biased. Our results suggest that civic AI can support preparation for public reasoning when it makes evidence, gaps, and disagreement open to inspection, and that corpus imbalance and two-sided personas remain open justice concerns.
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URL
https://arxiv.org/abs/2611.13525
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CulturalCrosswords: A Multilingual Benchmark of Answer Constraints and Grid Completion
2026-11-10 03:31:27Isaac Saxton-Knight, Gabriel Saxton-Knight, Diego Oliveira, Tidor-Vlad Pricope, Aston Garratt, Jocelyn D'ArcyAbstract
When several answers fit a riddle, exact-match scoring can penalise a valid response. CulturalCrosswords tests how answer constraints affect large language models (LLMs) solving cultural riddles, whose solutions draw on knowledge specific to a community. We constructed 53 crosswords from crowdsourced riddles in 42 languages and evaluated 11 LLM configurations on five versions of each clue set: plain batches, batches with lengths, batches with lengths and partial-answer hints, and crosswords requesting an answer list or a completed grid. On 962 clues with alternative answers, lengths improve agreement with the reference by 22.8 percentage points, compared with 8.4 points when accepted alternatives also receive credit. Part of the gain therefore appears to come from selecting a required answer form. Crossword input increases the share of all crossings with compatible answers by 9.7 points; requiring a completed grid reveals large differences between models. The benchmark distinguishes agreement with a reference from the ability to produce answers that fit together and occupy the correct cells.
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URL
https://arxiv.org/abs/2611.10044
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The WoW: A Large-Scale World Knowledge Corpus for Full-Lifecycle LLM Training
2026-11-10 02:56:20Tianze Sun (Harbin Institute of Technology), Yongyao Duan (OpenCSG), Sam Chen (OpenCSG)Abstract
Existing Chinese educational corpora often suffer from fragmented coverage and a lack of systematic organization. In this paper, we propose WoW (\textbf{Wo}rld Kno\textbf{w}ledge), a large-scale dataset designed for the full-cycle training of foundation models. The construction of this dataset is driven by an Educational Knowledge Graph, which serves as a taxonomic backbone for the systematic collection and completion of missing educational modules across all levels. Compared to previous versions, this iteration provides enhanced systematicity, superior quality, and a larger scale, while incorporating multimodal signals and supporting every stage of model development. Experiments demonstrate that WoW significantly improves model performance across diverse benchmarks. To facilitate community research, we further provide an interactive platform for graph-based data selection and retrieval.
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URL
https://arxiv.org/abs/2611.13487
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Inherited Weights: Measuring the Tokenization Premium and the Sovereignty Cost of Language Models for the Global South
Abstract
Most language models deployed outside the United States and China are fine-tuned descendants of a small number of pretrained families. We argue that fine-tuning leaves the decisive properties of a model inherited from its base: vocabulary, knowledge, cultural alignment, memorized data and license. We contribute (i) an updated, reproducible measurement of the tokenization premium across 31 languages and 7 tokenizers released in 2023-2024, on 27 articles of the Universal Declaration of Human Rights, with explicit correction for unknown-token collapse. Premiums reach 10x for Amharic in older tokenizers and remain at 6.8x in the newest commercial one; Indigenous South American languages pay between 1.6x and 2.7x under global tokenizers. A vocabulary built from Portuguese lowers the premium for 11 of 13 Latin-script Global South languages relative to every global tokenizer tested, by about 24% for Guarani and Quechua. (ii) An eight-test black-box sovereignty audit that a buyer can run without access to weights. (iii) A regime argument: under energy and liability constraints, accuracy per joule, calibration and selective risk should replace raw accuracy as design objectives, with metrics that abstention cannot inflate. We close with preliminary internal measurements from a proprietary architecture built around these objectives, reported with their limitations. Data and measurement code are included.
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URL
https://arxiv.org/abs/2611.13441
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BioMem: Agentic Memory for Biomedical LLM Agents
2026-11-10 01:09:11Mingchen Li, Yongkang Xiao, Rumeng Li, Rui Zhang, Hong YuAbstract
Large language models (LLMs) have demonstrated considerable potential in biomedical applications, while agentic memory systems have emerged as a promising approach to preserving and utilizing information across tasks and interactions. However, a fundamental question remains: how effectively and reliably do these systems retain, retrieve, update, and utilize biomedical information? In this study, we systematically investigate agentic memory in the biomedical domain, focusing on task effectiveness, retrieval quality, information fidelity, and long-term robustness. We evaluate five memory approaches across four LLMs in three representative settings: biomedical knowledge reasoning, clinical prediction, and long-term conversational memory. Our analyses reveal how memory construction, evidence relevance, and conversational history shape downstream biomedical performance, highlighting the distinct challenges of retrieving relevant information, preserving its fidelity, and effectively utilizing it for reasoning. Building on these findings, we introduce Hybrid Memory Retrieval and Utilization (HMRU), a training-free framework that jointly enhances evidence accessibility and task-aware utilization to improve biomedical reasoning and long-term conversational memory. HMRU achieves an average relative improvement of 8.6% across biomedical question-answering and conversational memory benchmarks, with additional gains in selected clinical prediction tasks. It further reduces conversational answer errors by 24.9% and closes 48.0% of the performance gap to full-context inference. Our findings provide a systematic characterization of biomedical agentic memory and practical insights into developing more effective and reliable memory-augmented LLM systems.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13426
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Fastener-centric acquisition and AND/OR graph representation of state-dependent disassembly knowledge from CAD-derived virtual assemblies
Abstract
Design-stage disassemblability assessment requires knowledge of product structure and the operations available in each assembly state, but CAD assemblies do not directly specify state-dependent disassembly operations. This paper proposes a fastener-centric virtual-disassembly framework that acquires such knowledge from CAD-derived virtual assemblies. Fasteners jointly releasing the same constraint are grouped into operation-level sets, avoiding enumeration of arbitrary individual-fastener orders. Their removability is evaluated through state-dependent visibility and tool-access checks and torque-driven virtual-removal trials. After fastener removal, graph-based reasoning identifies candidate moving units, whose separation is evaluated by initial-motion screening, path generation, and physics-based validation. The acquired operations, states, dependencies, validated paths, and simulation-derived attributes are organized into a Transformed AND/OR Graph for subsequent planning and design evaluation. Exhaustive verification with a controlled toy assembly found no branching violations for introduced tool-accessibility and precedence conditions. Evaluation with a 54-component OpenManipulator Chain gripper demonstrated state-dependent fastener-removal and part- or subassembly-separation operations. Visibility and tool-accessibility evaluations pruned 51.4% of fastener-set candidate evaluations, while removal of obstructing components enabled previously inaccessible fastener operations. Operation-level grouping and screening thereby limited detailed physical validation to mechanically plausible candidates. The results demonstrate the feasibility of acquiring product-specific disassembly knowledge from CAD-derived virtual assemblies before manufacture, supporting subsequent disassembly reasoning and design-for-disassembly evaluation.
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URL
https://arxiv.org/abs/2611.13379
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Mapping Instructors' Attitudes Towards Cheating with AI: An Exploratory Analysis Using Forum Analysis
2026-11-09 23:39:11Iman Mohammadi, Ali Keramati, Sina RismanchianAbstract
In teaching and learning environments, cheating and the violation of academic integrity have always been one of the challenges, involving different levels of concerns such as administrative, personal knowledge, learning, and equity. The introduction of large language models has brought cheating in dialogue to the forefront, given the apparent capabilities of these models to complete students' academic assignments. This study aims to leverage recent dialogues among university professors about cheating with Generative AI to better understand their beliefs about academic honesty. In doing so, we analyze a corpus of 2.01 million posts (threads and comments) in the r/Professors subreddit discussing cheating after the release of ChatGPT, providing a better understanding of teachers' motivations to discourage cheating and the strategies they use to do so. We synthesize the why and how of addressing cheating to identify caveats about the future of AI and academic integration.
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URL
https://arxiv.org/abs/2611.13366
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PegasusAI Studio: Legible Cyberinfrastructure and Accountable AI Agents for Scientific Workflows
2026-11-09 22:22:25Komal Thareja, Mats Rynge, Berent Aldikacti, Angelina Ju, Ian Lumsden, Anirban Mandal, Rajiv Mayani, Kin Wai Ng, Hamza Safri, Michael Stealey, Sai Swaminathan, Michela Taufer, Karan Vahi, Michael Zink, Ewa DeelmanAbstract
AI coding agents can now construct scientific workflows, but operating them on shared cyberinfrastructure (CI) raises problems that code generation does not solve. Agents need current knowledge of the workflow system and its execution sites and access to the runtime state of distributed executions, while facilities need to attribute every agent action to a user and bound it by that user's privileges. We present PegasusAI Studio, an AI-assisted science gateway built on the Pegasus and HTCondor stack. Studio makes CI legible to agents by projecting one facility-curated knowledge base into the native configuration of several coding agents, by introspecting workflow interfaces and execution sites, and by grounding diagnosis in workflow and scheduler telemetry. Agent actions operate within a trust envelope that combines federated authentication, per-user execution environments and scheduler credentials, provider-neutral model access, observability, and approval controls for state-changing actions. The same application runs as a JupyterHub-based gateway on a research cloud and as an Open OnDemand application on HPC systems. We evaluate Studio through case studies of legacy CASA weather workflow modernization, a gravitational-wave reanalysis, an EEG analysis built with an open-weight model, and an Event Horizon Telescope (EHT) M87 image reconstruction that recovered from two failed runs to a zero-failure execution. The evaluation establishes that these mechanisms operate as designed.
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URL
https://arxiv.org/abs/2611.13309
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MRFoundry: Knowledge-guided agent loops for end-to-end MRI research
2026-11-09 21:54:54Ke Wang, Atakan Topcu, Hossein Momeni, Molin ZhangAbstract
AI agents can write code, use scientific software, and revise computational workflows, opening new possibilities for MRI research. Effective assistance requires connecting acquisition physics, data conventions, specialized tools, and scientific validation. We present MRFoundry, an open-source, extensible package that connects MRI knowledge to agent actions and output checks. Project-local memory preserves decisions and evidence, while reviewed contributions allow researchers to improve shared guidance. Researcher-supervised examples demonstrate skill-guided sequence auditing and acquired-data reconstruction, connecting tool use, output checks, and documented decisions. Alongside MRFoundry, we introduce MRI Agent Bench to compare agents with and without the package across models and MRI task domains. Across 240 paired Core evaluations spanning 24 task types and ten models, all-checks-passed completion increased from 67.5% without MRFoundry to 78.3% with it. Across 134 Hard pairs, completion increased from 36.6% to 44.8%. MRFoundry makes MRI workflow expertise available as reusable guidance that researchers can inspect, adapt, and extend within existing agent and scientific software environments. MRFoundry is openly available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13289
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Decisions by Discrimination: Differential Evidence for Medical Reasoning
2026-11-09 21:02:23Yunpeng Xiao, Yuzhang Xie, Junhao Huang, Carl Yang, Xiao Hu, Kai ShuAbstract
Large language models (LLMs) are increasingly studied for clinical decision support, and medical question answering provides a testbed for evaluating this capability. In particular, medical multiple-choice questions (MCQs) reflect a core challenge in clinical decision-making: plausible options often share overlapping evidence, while only a few decisive factors distinguish the correct option from incorrect ones. However, existing retrieval-based and reasoning-oriented methods mainly focus on supplying broadly relevant knowledge or eliciting reasoning paths, rather than identifying the compact differential evidence that separates confusing alternatives. In this work, we present a retrieval-grounded differential evidence framework for medical MCQs. Our method extracts option-specific pieces of differential evidence and organizes them into concise evidence prompts for answer selection. This design better aligns external knowledge with differential reasoning while reducing irrelevant context. Experiments show consistent accuracy gains across multiple datasets and target LLMs. Ablation studies further show that our framework outperforms both directly using retrieved evidence chunks and closed-source LLM-generated general hints, and that its improvements are not solely explained by answer-proximal guidance, but are consistent with differential evidence serving as a compact aid for reasoning over competing alternatives. Our findings also offer insights for medical reasoning and other decision-making settings with multiple possible choices.
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URL
https://arxiv.org/abs/2611.13233
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Deletion Sensitivity Is a Property of the Model: A Three-Condition Measurement across Seven Open Reasoning Models
2026-11-09 20:51:21Hanzhang Jia, Liheng Zeng, Zhiyu Yang, Yi Gao, Bo MaAbstract
Reasoning models write a reasoning trace at every turn of a multi-turn tool session, and later turns read this history. Across 94k ShareGPT conversations, 73 percent of user interactions span multiple turns. Upstream providers bind each trace to its issuer, so a request forwarded across issuers arrives with its history deleted, and the task cost of this stripping remains unmeasured. To the best of our knowledge, this paper presents the first direct, blind, multi-seed measurement of full reasoning-history deletion in multi-turn tool calling, through three rendering conditions, seven open-weight reasoning models, three seeds, 794 tasks, and the official checker. The same deletion spans -4.4 to +51.3 points with both ends excluding zero, and for the one model re-dosed from 0 to 100 percent deletion, the reading varies by at most 1.5 points. Deletion sensitivity is a model-level property. The reading follows the model itself and stays constant across deployments, and a retrained or re-released model is a new model that a two-cell screen, one retention run and one deletion run, re-reads for the cost of two runs. Deletion sensitivity is thus a measurable property of the model, and the reading must enter routing and aggregation as prior information rather than persist as an invisible default cost.
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URL
https://arxiv.org/abs/2611.13223
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Evaluating Long-Horizon Robot Planning under Uncertainty
2026-11-09 20:46:30Harsh Muriki, Gayathri Aravindan, Yuchen Zheng, Sonia ChernovaAbstract
Household robots must successfully navigate im-perfect circumstances, both in their internal models and the external world. We introduce a benchmark of seven long-horizon household tasks in Habitat that vary initial robot memory and environment configurations. We evaluate adaptations of ReAct, VLM-TAMP, and Tru-POMDP within a shared execution framework across 90 tasks-environment-memory combinations, totaling 1,350 episodes. Our evaluation measures task success, partial completion, and action efficiency, along with rule-based analysis of execution traces. ReAct achieves the highest overall task success (45.3%) and completion (71.7%), while having accurate initial memory does not consistently yield the highest performance for every planner. We identify recurring placement errors, precondition errors, and redundant actions through trace analysis. These findings highlight differences in how the evaluated planners use initial knowledge and respond to uncertainty.
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URL
https://arxiv.org/abs/2610.17326
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