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新论文 · v1arXiv:2610.39100

Generating Critic-Guided Synthetic Experience in Latent World Models

Jorge de Freitas, Christos Ziakas, Alessandra Russo

原文摘要

Learning from offline datasets is limited in scalability, as collecting new experience is often costly or unsafe in the real world. World models offer an alternative way to generate new experience through a learned dynamics model, yet learning policies in latent space is limited due to compounding errors in imagined rollouts. To this end, we propose using the world model as a local transition generator, leveraging critic guidance to choose actions inside that model to generate synthetic experiences. We study action refinement methodologies, introducing Critic-Guided Synthetic Experience (CGSE) generation in world models. Specifically, CGSE augments an existing offline dataset by sampling candidate actions using an offline policy and refining each to maximize a first-order approximation of the $Q$-function within a trust region. We then use a frozen latent world model to imagine the future state given each updated action, and use the critic to choose the best one, appending this synthetic transition to the replay buffer. Our method requires no additional real-environment interaction or test-time planner, as it generates synthetic experience offline using the world model. On three OGBench manipulation tasks, CGSE improves an offline policy by a relative improvement of $7.1\%$ on the average success rate.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.14867

Evaluating the Robustness of Proof Autoformalization in Lean 4

Zhengtao Gui, Sheng Yang, Zhouxing Shi

原文摘要

Proof autoformalization aims to translate a mathematical informal proof in natural language into a formal proof in a formal language such as Lean 4. Existing LLM-based models for proof autoformalization are typically evaluated only on well-formed informal proofs from curated datasets. We argue that a robust proof autoformalizer must remain faithful even for informal proofs that diverge from these idealized ones, and we present the first study on the robustness of proof autoformalization models. We formulate two categories of perturbations: a global perturbation paraphrases the informal theorem-proof pair in a different style, under which the formalization should remain consistent; a local perturbation alters a value, symbol, or proof step, possibly in a counterfactual way, and a robust formalization should faithfully reflect the perturbation rather than reverting to the original or inferring a different one on its own. We build a benchmark with both perturbations on miniF2F and MATH-500, and automatically measure how stable a proof autoformalization's correctness is under global perturbations and how faithfully its output reflects local perturbations. We evaluate seven recent models: their correctness varies under global perturbations, and they mostly fail to remain faithful under local perturbations.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39090

PALATE: Pretrained Autoregressive LATent Embeddings

Sizhang Zhao, Praveen Gondhi, Bin Li, Utsaw Kumar

原文摘要

Local-commerce and food-delivery marketplaces run large ads businesses: across hundreds of thousands of stores, merchants pay to be surfaced on browse and search surfaces, and a ranker decides which ads each consumer sees. Ranking well demands strong per-consumer personalization, yet a consumer's raw interaction history is sparse and high-cardinality. Our production ranker had personalized only through hand-engineered signals: aggregate engagement counts, categorical embeddings, and order-agnostic pooled history, with no learned per-consumer sequence representation. We learn one by training a GPT-style generative model of the consumer's engagement sequence to predict their next few engagements, and read its last-token hidden state as the consumer embedding. That single frozen vector becomes one shared personalization input for every ad surface: a daily job refreshes it, and each ranker consumes it as an ordinary dense feature. Offline it is the largest single-feature gain in our ranking-AUC ladder, a relative Eval-AUC gain of +0.59% on a browse-driven feed and +0.57% on a query-driven search surface. Online, it shipped as one component of bundled ranker retrains on both surfaces: Discovery (revenue +0.79%, conversion rate +1.17%) and Search (revenue +0.94%, conversion rate +1.50%). Three design choices adapt the generative objective to a repurchase-heavy marketplace, and are the core of our contribution: an exploration-weighted loss that up-weights businesses new to the consumer over ones already engaged; a two-stage curriculum: a click pretrain that keeps the representation broad and exploratory, then a conversion fine-tune that sharpens purchase intent; and a designed negative set that pairs a fixed popular-business pool and businesses shown but not ordered with random and in-batch negatives.

cs.IR · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39086

HALO: Human-Grounded Granular Evaluation of Proactive Agents

Wenqi Pei, Xinyu Wang, Jinrui Liu, Cynthia Zhuge, Jie Sheng, Zihan Wang, Tenghao Huang, Muhao Chen

原文摘要

Proactive agents must decide whether, when, and how far to assist without an explicit request. Existing evaluations often rely on automated scores or binary verdicts, offering limited human diagnosis of evidence use and intervention quality. To address these gaps, we introduce HALO, Human-Grounded GrAnular EvaLuation of PrOactive Agents. HALO-Arena covers 4 proactive need types, Dependency, Blockage, Risk, and Opportunity, across 10 everyday domains and 50 personal environments. Scenarios require agents to retrieve evidence from structured databases or unstructured documents before judging whether assistance is warranted and choosing its scope. On HALO-Arena, we construct HALO-8K, a dataset of 8,000 responses from 10 models across 400 scenarios, annotated by 20 experts for action level, acceptability verdicts, and human diagnosis. For rejected responses, human diagnosis covers timing, intensity, and other issues. Recorded reasoning tokens and retrieval rounds measure the effort behind these responses. The best proprietary and open-source models achieve 72.88% and 56.63% acceptability, respectively. Our central finding is that over-proactivity is the dominant observed failure mode, with Premature timing or Excessive intensity accounting for 61.20% of rejected responses. Further analyses yield complementary findings on the balance between intervention and restraint, evidence acquisition, and the calibration of timing and intensity.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.07424

An Analysis of Artificial Intelligence Adoption in NIH-Funded Research

Navapat Nananukul, Mayank Kejriwal

原文摘要

Understanding the landscape of artificial intelligence (AI) and machine learning (ML) adoption across the National Institutes of Health (NIH) portfolio is critical for research funding strategy, institutional planning, and health policy. The advent of large language models (LLMs) has fundamentally transformed research landscape analysis, enabling researchers to perform large-scale semantic extraction from thousands of unstructured research documents. In this paper, we illustrate a human-in-the-loop research methodology for LLMs to automatically classify and summarize research descriptions at scale. Using our methodology, we present a comprehensive analysis of 58,746 NIH-funded biomedical research projects from 2025. We show that: (1) AI constitutes 15.9% of the NIH portfolio with a 13.4% funding premium, concentrated in discovery, prediction, and data integration across disease domains; (2) a critical research-to-deployment gap exists, with 79% of AI projects remaining in research/development stages while only 14.7% engage in clinical deployment or implementation; and (3) health disparities research is severely underrepresented at just 5.7% of AI-funded work despite its importance to NIH's equity mission. These findings establish a framework for evidence-based policy interventions to align the NIH AI portfolio with health equity goals and strategic research priorities.

cs.AI · cs.CY · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.01033

The Model Organism Lottery: Model Organism Interpretability Strongly Depends on Training Methodology

Andrzej Szablewski, Gabriel Konar-Steenberg, Raffaello Fornasiere, Nikita Menon, Stefan Heimersheim

原文摘要

Model organisms (MOs) - language models trained to exhibit undesired or unnatural behaviours - are frequently used as testbeds for evaluating white-box interpretability techniques. Current MOs are typically constructed via post-hoc supervised fine-tuning (SFT). Prior research has shown that interpretability methods can easily identify hidden behaviours in these MOs. However, recent work suggests that such post-hoc training methods may make interpretability unrealistically easy. We investigate this claim by constructing a suite of 61 $\verb|OLMo2-1B|$- and $\verb|gemma-3-1b-it|$-based MOs trained with seven different techniques, including standard post-hoc SFT, post-hoc DPO, and more realistic integration of MO data into the OLMo post-training DPO phase. We also use these to train 96 further variants via knowledge distillation. We use these MO variants to benchmark activation oracles, activation steering, the logit lens, and the Jacobian lens. Our findings show that (i) MO interpretability depends strongly on training objective, target behaviour, model architecture, and training data generation pipeline; (ii) substantial variance remains even after controlling for differences in the strength of target behaviour expression; and (iii) both our more realistic $\textit{integrated training}$ and knowledge distillation often yield less interpretable MOs than standard post-hoc methods. Our results cast substantial doubt on the validity of current MOs as interpretability proxies and suggest actionable steps towards more robust benchmarks.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39074

HERA: Assessing an Autonomous Agent for Quantum Device Measurement

Zachary W. Gomez, Nathan Bittner, Jordan T. McCourt, Qingrui Cao, Sandesh S. Kalantre, Benjamin H. Alexander, Zhiren Zheng, Kenji Watanabe 等 10 位作者

原文摘要

Large language model agents have begun to carry out laboratory measurements, but their scientific conclusions are useful only if they hold up under scrutiny. Demonstrating this requires controlled studies because models are stochastic and the context a model is given steers its conclusions. Here we introduce HERA, an autonomous agent designed to carry out cryogenic transport measurements of quantum devices. HERA draws on a knowledge base curated by lab members that holds measurement protocols, software tools, and literature references. We assessed HERA with a blinded trial against an expert on the stacking order of two multilayer graphene devices, an ablation of its knowledge base, and hundreds of offline replays in which models designed the trial's central measurement without running it. HERA's conclusions matched the expert's sealed conclusions on both tested devices. The ablation and the replay studies measured the impact of the knowledge base across different models, showing that with it every model designed better measurements and reasoned more soundly. To test how readily HERA extends to a new problem, we tasked it with finding and characterizing superconductivity in a different material system. This required a few hours of expert time to develop a new experimental protocol, after which HERA did both autonomously. These results show that, with a knowledge base designed for the task, language model agents are capable of sound experimental science.

cond-mat.mes-hall · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39073

Certified Access: Test-Time Compute on Conservative Physics-Structured AI Memory

Pratik Jawahar, Riccardo Maggioni, Jonas Petersen, Matteo Cozzi, Gian-Alessandro Lombardi, Marek Gajewski, Camilla Mazzoleni, Federico Martelli

原文摘要

Test-time compute mechanisms, such as retries, confidence-based escalation and non-local routing, are conventionally deployed as black-box additions to trained models. They typically add forward passes or halting heads, but give no structural account of what the extra compute buys or what it costs against the model's stability guarantees. We therefore propose a theoretical and empirical framework for test-time compute on a Latent Dynamics Model used as the memory. When an encoder's latent state $(q,p)$ is advanced by a damped symplectic (velocity-Verlet) step of a learned Hamiltonian, every mechanism that expands access has an exact phase-volume Jacobian, a bounded energy change and an exact latch-transport law. We split reachability failures into escape failures and reach failures. A Lorentz squeeze resolves escape failures by injecting bounded, re-absorbable energy. A gated canonical translation (a one-step channel across $C_T$) resolves reach failures at $\detJ=1$ exactly. We verify this certificate stack on a designed analytic testbed. Reaching just beyond $C_T$ with the squeeze requires energy that grows quadratically in the excess distance. The gated channel's energy cost equals the potential difference between its endpoints, which is zero on the matched channel measured here, and so does not grow with distance in this testbed. An untrained state-replacing map annihilates phase volume and erases stored charge, while the gated channel transports it. On a learned physics-structured memory, we show calibrated compute rationing and evaluate the scaling limits of non-local routing against a physics-free baseline. The main contribution is the formalization and verification of the certificate stack, not benchmark performance.

cs.AI · hep-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39072

PhysicsNeMo-Mesh: GPU-Native Meshes and Simulation Cases for Physics AI

Peter D. Sharpe, Corey Adams, Negin Sobhani, Sanjay Choudhry

原文摘要

PhysicsNeMo-Mesh is the mesh module of the open-source PhysicsNeMo library. It provides mesh processing similar to PyVista, but on the GPU, in batches, and inside PyTorch's automatic differentiation. Like xarray for unstructured meshes, it attaches semantics to data: fields on points, cells, and whole meshes have names and shapes, and a simulation case keeps the complete PDE problem specification, including boundary conditions and their prescribed values. It addresses three problems that plague machine learning for physical simulation. First, GPU utilization is bottlenecked by I/O, because samples are very large relative to the compute per forward pass. Second, because traditional preprocessing is expensive, it is necessarily done once, during curation, rather than differently for each model and workflow at the point of use. Finally, advanced equivariant and mesh-native architectures require information that curation ordinarily discards. Fast GPU mesh processing makes it practical to preprocess at the point of use: on one GB200 GPU, mesh operations run $6.2\text{--}1{,}068\times$ faster than VTK with 72 threads, and loading a whole case is $8.0\text{--}88\times$ faster than reading its VTK files ($8.0\times$ when both read the same fields). Overlapped with training, this preprocessing costs $2.1\%$ of throughput for a $127\,\text{ms}$ Transolver step. So one dataset serves many models and workflows: in PhysicsNeMo's public recipe, one stored DrivAerML dataset feeds Transolver, GeoTransolver, and GLOBE. PhysicsNeMo-Mesh combines the speed of an intermediate array store with the flexibility of an archival format such as VTK. When shapes and wall conditions both vary, per-boundary conditions cut a heat-conduction error from $87\%$ to $6\text{--}9\%$. And by keeping field ranks and named boundaries, it retains the semantic structure that next-generation architectures will need.

physics.comp-ph · cs.CG · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.04262

Can I Take Another Dose? DoseBench: Evaluating LLM Decision-Making in OTC Dosing QA

Maroof Kousar, Jinsung Han, Mohit Chandra, Munmun De Choudhury, Yibo Hu

原文摘要

Large language models (LLMs) are increasingly used to answer everyday health questions online, including whether someone can take another dose of an over-the-counter (OTC) medication. Yet this common safety-relevant setting remains underexplored in medical QA. Answering these questions requires checking previous doses, timing, and product-specific dosing limits, while recognizing when important information is missing. We introduce DoseBench, a focused benchmark of 230 consumer-style acetaminophen and ibuprofen dosing questions with human-annotated YES, NO, and AMBIGUOUS labels, and evaluate eight LLMs with five responses per question. Overall accuracy ranges from 44.8% to 62.2%, while models incorrectly approve 14.0% to 60.5% of prohibited doses. Models with similar accuracy can therefore make very different dosing decisions. Additional analyses show that these failures are not easily fixed by giving models more guidance. Asking models to check dose, timing, and missing information makes them more cautious, while providing drug facts improves accuracy but can make them overlook missing information. These findings highlight a broader challenge in health QA: applying rules correctly and recognizing when information is missing.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39068

Closed Gates Leak: Decomposing Adjacent-Layer Cancellation in Gated Language Models

Harvey Dam, Ganesh Gopalakrishnan

原文摘要

In transformer language models with gated multilayer perceptrons (MLPs), adjacent layers write partly against each other into the residual stream. Previous work interpreted this cancellation as layers proposing features and the next layer selectively rejecting the inappropriate ones. We decompose the cancellation by removing the previous layer's write from the input of the next MLP: what the next layer writes along that write splits into what it writes anyway and how much less its MLP writes because the write is present, which we call the damping, and we trace the damping to the neurons that carry it. In gated MLPs the damping is carried by neurons whose gates are closed. SiLU and GELU are slightly negative below zero, so a closed gate still passes a small negative multiple of what the neuron reads, and a neuron whose output weight points along its input weight then subtracts a fraction of the direction it read. Summed over such neurons, closed gates contract the average direction a gated MLP reads, and the previous layer's write is not singled out: the same layer's write at another token is damped as much or more, and the eigenvalue signature from which that work concluded that the correction is selective appears for another token's write as well. Over training the damping grows while what the next layer writes anyway stays flat, and cancellation begins when the damping overtakes it; annealing the negative side of the gate activation away in continued pretraining removes the damping and the cancellation. Adjacent-layer cancellation is therefore weak evidence about which features a model rejects: it is produced by a contraction that gated MLPs apply, on average over directions, to whatever they read.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.03529

Incentives Shape How Humans Co-Create with Generative AI

Nathanael Jo, Manish Raghavan

原文摘要

Generative AI is quickly becoming an integral part of people's everyday workflows. Early evidence has shown that while generative AI can increase individual-level productivity, it does so at the cost of collective diversity, potentially narrowing the set of ideas and perspectives produced. Our research stands in contrast to this concern: through two pre-registered randomized control trials, we show that incentives can moderate AI's homogenizing force in two creative writing tasks where participants can use AI interactively. Participants rewarded for originality relative to peers produce collectively more diverse writing than those rewarded for quality alone. These participants continued to engage with AI, but ultimately incorporated less AI-generated text verbatim. Our results reveal that the effects of generative AI depend not only on the technology itself, but also the behavioral strategies and incentive structures surrounding its use. We release a rich, anonymized dataset capturing fine-grained trajectories of human-AI co-creation across both studies.

cs.HC · cs.AI · cs.CY

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.10319

Do Personalized Skills Help Coding Agents? An Empirical Study of Developer Interaction Traces

Shuyan Huang, Kai Du, Andrew Lan

原文摘要

As human developers increasingly collaborate with large language model (LLM)-powered coding agents, adapting to individual developer preferences, potentially via agent skills, may offer way to reduce repeated guidance and improve collaboration efficiency. However, existing approaches primarily focus on building skills for each task rather for each human developer. In this work, we propose a framework that combines rule-based bootstrapping with LLM-based refinement to distill developer-specific skills from their interactions with coding agents. We further develop an interactive replay framework with an LLM-based developer simulator for evaluation. We conduct experiments on a collection of replayable real-world developer-agent interaction sessions. We find that overall, task-specific skills are the most effective; However, personalized skills become more effective when developer preferences are manifested with enough frequency, especially when developers work on tasks that are highly related. These findings provide empirical insights suggest that i) broadly transferable procedural knowledge can be more robust than developer-specific preference signals and ii) effective personalization requires mining from rich user preference signals.

cs.SE · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.00414

Toward Autonomous Laboratory Safety Monitoring with Vision Language Models: Learning to See Hazards Through Scene Structure

Trishna Chakraborty, Udita Ghosh, Aldair Ernesto Gongora, Ruben Glatt, Yue Dong, Jiachen Li, Amit K. Roy-Chowdhury, Chengyu Song

原文摘要

Laboratories are prone to severe injuries from unsafe actions, yet continuous manual safety monitoring, beyond mandatory pre-lab safety training, is infeasible. Vision-language models (VLMs) offer promise for autonomous laboratory safety monitoring, but their effectiveness in realistic settings is unclear due to the lack of visual evaluation data. Safety incidents are mostly documented as text, and existing studies are largely limited to textual understanding. Since hazardous scenes are unsafe to stage or capture, synthetic visual data becomes the only practical way to study this problem. This motivates us to introduce a structured data generation pipeline that converts textual laboratory scenarios into aligned quadruples using LLM as scene graph architect and image generation model as renderer, yielding \dataset: a synthetic dataset of 1,207 $\langle$image, scene graph, ground truth, hazard relevance mask$\rangle$ quadruples across 362 unique scenarios. Experiments on \dataset and seven open- and closed-source models show that VLMs perform effectively given a textual scene graph but degrade substantially in visual-only settings (i.e., where no scene graph is involved), indicating difficulty in extracting structured object relationships directly from pixels. We thus propose \saga, a post-training framework that leverages reinforcement learning to first structure the visual scene as a scene graph, and then predict hazards while aligning attention with relevant image regions. Evaluations with Qwen 3.5 and Gemma 4 show that our method nearly doubles the $F_1$ score over the baseline on our synthetic dataset and achieves an improvement of $\sim 3\times$ on a mini real-world dataset, while preserving general visual understanding in visual question answering.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39065

Tabular foundation models are not a shortcut to calibrated clinical risk prediction: a pre-registered simulation against the true risk

Ebrahim Khaled Ebrahim

原文摘要

Tabular foundation models are pretrained networks that predict risk from a table without fitting a model. Whether their risks are correct (calibrated) has been judged on real data, where true risk is unknown. In a pre-registered simulation with known true risks, we compared TabPFN-3.5, TabICLv2 and Mitra-v2 with Firth logistic regression with intercept correction (FLIC) in 58 scenarios and 1,000 random designs, then on four clinical data sets. Foundation models were not a shortcut to calibration. Their calibration slope matched FLIC's within the pre-registered margin in 36 or 37 of 48 core scenarios and was better only at n = 200 with a 3% event rate. Even 3,000 patients were often not enough: with FLIC, TabPFN-3.5 or Mitra-v2, 63%-83% of samples gave risks close to the truth in average, intercept and slope at event rates of 10% and 30%, and 35%-50% at 3%. TabICLv2 did worse (8%-39% and 1%-3%). Across the random designs, foundation models had a lower true calibration error than FLIC beyond the margin in 25%-38% and a higher one in none, though small losses were predicted at n = 3000. On real data, no model resisted a change in event rate between populations; recalibration on 500 local patients largely repaired it.

stat.ME · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39060

Tracing the Influence of Retrieved Literature on Scientific Ideation in LLMs

Zhipin Wang, Koel Dutta Chowdhury, Christoph Leiter, Steffen Eger

原文摘要

Large language models (LLMs) are increasingly used to generate research ideas from scientific literature. Existing systems combine literature retrieval with search, generation, and refinement, making it difficult to determine how the retrieval strategy itself shapes the ideas generated. We isolate this contribution by comparing five retrieval strategies and a No Literature baseline under a shared generation protocol, using 45,000 ideas across 15 machine learning topics and ten open-weight models. Relative to no literature basline, all five retrieval strategies reduce diversity among ideas generated from the same set of papers by $5.6\%$--$17.9\%$, while most increase diversity by $10.2\%$--$18.1\%$ when ideas from different sets are pooled. Effects on idea quality and novelty are modest on average but vary across topics. We further apply a JSD-based attribution method to trace the influence of individual input papers on each idea's motivation, core idea, approach, and validation. In $79.3\%$ of ideas, only one or two distinct papers receive the highest attribution scores across these four parts. These leading sources often recur across ideas generated from the same set of papers, and groups with more varied source attribution contain more diverse ideas ($r=0.70$). These findings motivate various sets of literature and the use of attribution to guide the choice of sources for subsequent ideas.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39056

When Does Closed-Loop Curriculum Adaptation Help GRPO? An Analysis on Countdown

Yuv Raj Pant, Moshiur Rahman Tonmoy, Kaiqun Fu

原文摘要

Reinforcement fine-tuning has become an effective post-training paradigm for improving the reasoning ability of large language models, but it remains sample-inefficient when the training distribution is mismatched to the model's current capability. In group-relative objectives such as Group Relative Policy Optimization (GRPO), problems that are too hard often produce uniformly failed rollouts, while problems that are too easy provide little useful advantage signal. In this work, we introduce CLOSEDLOOPGAUSSIAN, a lightweight adaptive curriculum method for efficient reinforcement fine-tuning on ordered reasoning tasks. Instead of following a fixed easy-to-hard schedule, CLOSEDLOOPGAUSSIAN preserves the structure of an Easy-to-Hard Gaussian sampler while dynamically adjusting its pace and spread using periodic probe estimates of per-difficulty accuracy. The controller accelerates training when the model is ahead of a reference curriculum, slows down when the model falls behind, and broadens sampling when earlier difficulties regress. This feedback mechanism keeps training focused on tasks that are challenging but still learnable, without changing the reward function, model architecture, or underlying GRPO optimizer. Experiments on symbolic arithmetic reasoning task show that CLOSEDLOOPGAUSSIAN improves exact-match accuracy over vanilla GRPO and fixed open-loop curricula, with the strongest gains in intermediate-difficulty regimes where group-relative learning signals are most informative. Codes are available at https://github.com/yuvrajpant56/Closed_Loop_Curriculum.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.25752

Which Answers Agree? Evaluating Self-Consistency from Incomplete Logs

Zhengtao Gui, Zexi Chen, Weining Shen

原文摘要

Self-consistency answers a question by generating multiple responses and selecting the most frequent answer. Its evaluation requires both correctness labels and information about which responses agree; correctness labels alone generally determine neither voting accuracy nor the benefit of additional votes. For tasks in which all correct responses map to the same normalized answer, we derive sharp accuracy bounds using classical stochastic majorization, refined by abstention counts and answer-category limits. We also derive sharp bounds on accuracy changes across voting budgets that share a response pool. When answer-category counts are available, we introduce Plurality-U, a count-based dynamic program that exactly averages voting accuracy over all fixed-size subsets, eliminating finite-pool Monte Carlo error. On 1,403,520 stored outputs from recent reasoning models in Scorio Lite and Scorio Trace, recording abstention counts and answer options cuts the width of SuperGPQA accuracy intervals by more than half. For moving from 9 to 17 votes, our bounds reduce interval width by a median of 77.7% on SuperGPQA and 72.3% on 14 GPQA and Adaptive-Consistency pools, compared with a baseline that combines separate accuracy bounds. Exact computation has lower paired median runtime than 2,000-subset Monte Carlo in 14 of 15 tested pool-budget settings. These results clarify what incomplete logs can establish and provide an exact evaluation procedure when answer counts are retained.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39055

Controlling Continuum Robot Shape in Enclosed Space with Embedded Sensing using IMU and Actuator Feedback

Jiahe Wang, Sultan Haidar Ali, Eric Weissman, Jiefeng Sun

原文摘要

Controlling continuum robots' shape to operate in confined, enclosed environments is challenging, since it cannot rely on external observation. Existing embedded shape sensing methods, although they can provide shape reconstruction, are insufficient to enable real-time shape control due to limitations in reconstruction accuracy, computational speed, and compatibility with shape-state representation. In this work, we present an embedded shape-sensing framework that uses readily available IMU and actuator feedback to reconstruct the continuum-robot shape with a neural network, providing accurate shape feedback at $400~\mathrm{Hz}$ for physical closed-loop control. We systematically investigate four key reconstruction design choices: shape-state representation, network architecture, training objective, and sensing inputs, through more than $5{,}000$ trained models on one- and three-segment continuum robots. The selected framework achieves global shape errors of $0.9\%L$ and $1.7\%L$ (of the robot length) for the one- and three-segment systems, respectively. In closed-loop control, one-segment tip tracking achieves a $6.1\%L$ error and three-segment shape control achieves a $2.3\%L$ global shape error, whereas IMU--PCC reconstruction using the same uncompensated measurements fails. Enclosed-space experiments further demonstrate operation without external observation. These results provide a practical framework for translating readily available embedded sensing into high-rate feedback for continuum robot closed-loop control.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39054

Robotic Data Inoculation

Qiyang Li, Zhiyuan Zhou, Charles Xu, Sergey Levine

原文摘要

Modern robot learning systems feed on high-quality demonstration data, and their performance hinges on coverage. Without good coverage, small errors compound and the policy can quickly go into out-of-distribution states from which it cannot recover. Yet there is no principled way of collecting high-coverage data, and data collection often relies on heuristic instructions to human operators. Purely online reinforcement learning (RL) explores at the cost of safety and sample efficiency, while DAgger-style interventions require the policy to fail by chance before a human can correct it. In this paper, we ask instead whether we can modify how the robot behaves during data collection to collect more useful data. We propose Data Inoculation (DI), a shared-control framework in which a human provide closed-loop teleoperation commands while a residual policy perturbs these commands. The residual policy is trained online, on the growing dataset, with the same reward-maximizing objective later used for policy extraction, so its perturbations steer data collection toward the deviations a policy trained on the data would make. These perturbations expose the robot to a wider range of states, while the human's continuous corrections provide recovery demonstrations, "inoculating" the dataset against the learner's future mistakes while the human remains in control. On three challenging real-world robotic tasks, DI consistently improves or matches the success rate of policies trained with both behavioral cloning and offline RL, showing DI as an effective data collection strategy for improving downstream policy performance.

cs.RO

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新论文 · v1arXiv:2610.39050

BASILISC: Bayesian Alignment of Siamese-Integrated Latents for Inferring Single-cell Correspondence

Sepideh Saran, Mahsa Ghanbari, Uwe Ohler

原文摘要

Single-cell assays study the genome one cell at a time. Unimodal assays measure one molecular layer, the newer multimodal assays measure several. Most data are single-modality, so integrating them relies on models trained on multimodal assays, where the cell correspondence across modalities is known. These models return a matching, but rarely a confidence measure. We present BASILISC (Bayesian Alignment of Siamese-Integrated Latents for Inferring Single-cell Correspondence), a framework for matching single-cell representations with uncertainty on the correspondence itself. A BASILISC model is a probabilistic siamese network over the two modalities with a predictive distribution over the correspondence for a cross-modal pair of cells. We instantiate it at two levels: at the cell-type level the question is whether two cells share a cell type, at the cell level which cell is the partner in the other modality. To our knowledge, this is the first uncertainty score on a learned cross-modal correspondence evaluated against a known ground-truth pairing. BASILISC learns a similarity across modalities that reflects cell lineage: an unseen cell type is matched to its lineage relative, matching still works on such held-out types (balanced accuracy 0.86 on unseen against 0.93 on seen types), and the predictive uncertainty rises on these out-of-distribution cells. The uncertainty score also selects high-quality matches for downstream analysis: discarding the least certain fifth of the pairs raises accuracy from 0.93 to 0.98. At the cell level, a deep ensemble of the deterministic matcher reaches 0.062 matching accuracy on the benchmark test set, above the competition winner; on the Bayesian matcher's assignment, the posterior flags the wrong matches that cross a cell-type boundary better than a deterministic read of the same scores (AUROC 0.68 against 0.64).

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39049

Reinforcement Learning for the Game of Balatro: A Fast Simulator and Two-Policy Training Approach

Ajay Vikram Periasami, Anjaneya Gupta, Ethan Cheng, Ivan Rosales

原文摘要

Balatro, a 2024 roguelike deckbuilder, presents three difficulties that have historically derailed reinforcement learning methods: a 3,000-action state-dependent action space, horizons of several hundred decisions with sparse rewards, and pervasive stochasticity in both card draws and shop generation. Published RL results on the game remain scarce. We introduce an end-to-end pipeline comprising (i) a high-throughput Rust simulator that is automatically state-diffed against the live game client via BalatroBot, (ii) a condensed observation-action interface with dynamic per-step masking of legal moves, and (iii) a multi-agent PPO architecture that decomposes play into a tactical blind policy and a strategic shop policy that share the full game state but act in disjoint phases. Long-horizon credit assignment is partially addressed by per-agent reward routing with explicit cross-agent credit. On the main joint run, training metrics show a sharp phase transition in which the first-blind clear rate converges above 99% and mean episode return increases by roughly an order of magnitude, plateauing at a mean maximum ante of 3 out of 8; single-agent baselines under identical rewards oscillated between greedy and hoarding failure modes and did not stably clear the first blind. These results establish the two-policy decomposition as a strong reactive baseline for Balatro and localise the remaining gap to long-horizon strategic planning rather than tactical blind play.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.00361

Attribution Gradients: Incrementally Unfolding Citations for Critical Examination of Attributed AI Answers

Hita Kambhamettu, Alyssa Hwang, Philippe Laban, Andrew Head

原文摘要

AI answer engines are a relatively new kind of information search tool: rather than returning a ranked list of documents, they generate an answer to a search question with inline citations to sources. But reading the cited sources is costly, and citation links themselves offer little guidance about what evidence they contain. We present attribution gradients, a technique to boost the informativeness of citations by consolidating scent and information prey in place. Its first feature is bringing evidence counts, supporting/contradictory excerpts, links to sources, and contextual explanations into one place. Its second feature is the ability to unravel second-degree citations in place. In a lab study, we demonstrate usage of the full gradient in a critical reading task and its support for deep engagement that increased the depth of what readers took away from the sources versus a standard citation and document QA design.

cs.HC · cs.AI

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arXiv ↗PDF ↗
版本更新 · v7arXiv:2509.25783

Sharpness of Minima in Deep Matrix Factorization

Anil Kamber, Rahul Parhi

原文摘要

Understanding the loss-landscape geometry near a minimum is key to explaining the implicit bias of gradient-based methods in non-convex optimization problems such as deep neural network training and deep matrix factorization. A central quantity for characterizing this geometry is the maximum eigenvalue of the Hessian of the loss. Currently, its precise role has been obfuscated because no exact expressions for this sharpness measure were known in general settings. In this paper, we present the first exact expression for the maximum eigenvalue of the Hessian of the squared-error loss at any minimizer in deep matrix factorization problems, resolving an open question posed by Mulayoff and Michaeli [2020]. This expression reveals a fundamental property of the loss landscape in deep matrix factorization: It is sufficient, but not necessary, for flatness that, across all layers, the product of the spectral norms of the left and right intermediate factors equals a constant determined by the depth and the spectral norm of the target matrix. Most notably, in both depth-2 matrix factorization and deep overparameterized scalar factorization, we show that this condition is both necessary and sufficient for flatness, which implies that flat minima are spectral-norm balanced and not necessarily balanced in depth-2 matrix factorization. To complement our theory, we provide empirical verification, relying on our exact expression, of an escape phenomenon during gradient-based training near a minimizer of a deep matrix factorization problem.

stat.ML · cs.LG · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39045

When Should AI Decide, Defer, and Deviate? Responsible Automation for Deceased Donor Kidney Allocation

Madeleine Pollack, Thomas Daillak, Mehmet Toner, Korkut Uygun, Heidi Yeh, Swati Gupta

原文摘要

We ask when an AI trained on historical decisions should be allowed to recommend decisions, defer to human experts, or in other cases, flag concerning patterns in data and deviate from past practice. This can be challenging because past decisions record what was done rather than what should have been done, and where data is sparse, a model's predictions may be too uncertain to act on. We provide a framework for addressing this problem using a case study in deceased-donor kidney allocation. Nearly 30% of kidneys recovered from deceased donors in the United States are never transplanted, largely because they are hard to place through the standard waitlist sequence. One can use AI to flag kidneys at risk of delayed or failed placement for out-of-sequence (OOS) allocation, but this comes at the cost of bypassing candidates higher on the waitlist. Thus, OOS allocation should only be used when the organ is demonstrably at high risk of discard on the standard sequence. Using Organ Procurement and Transplantation Network match-run data from 2016-2019 and 2023-2025 (excluding COVID-19-disrupted years), we build the Arbitrator framework, which combines various interpretable models and acts only when the uncertainty in their predictions cannot change the preferred action. Our Arbitrator resolves 93.5% of organ placement decisions using explicit medical criteria learned from data, and defers the remaining 6.5% to a human or a more complex model. We show that the Arbitrator correctly identifies 70.1% of historically discarded low-KDPI kidneys as OOS candidates, projecting that up to 43.9% of historically discarded kidneys could be flagged for expedited placement and potentially transplanted. We then exploit the interpretability of the Arbitrator to create an agentic audit comparing the learned rules to published clinical literature in order to flag decision-making patterns that are contentious.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.22686

Multimodal Filesystem Memory For Long-Horizon Embodied Agents

Chloe Su, Jiahan Li, Zhenting Qi, Chaoran Cheng, Haoyu Chen, Sejoon Jun, Weirui Ye, Yilun Du

原文摘要

Agents operating over long horizons need an effective memory that allows them to reuse their experience throughout long interactions. If the entire history of an interaction is retained, it becomes difficult to find the relevant experience, while compressing it into summaries may result in the loss of visual and spatial details that are required later on. We present \textbf{MFM}, a multimodal filesystem memory architecture that keeps complementary types of agent memory. \textbf{Episodic memory} stores the observations, actions, and outcomes, and \textbf{spatial memory} associates experiences with locations. \textbf{Semantic and procedural memory} build up general knowledge and reusable methods of acting, and \textbf{working memory} holds the current plans. MFM makes use of searchable textual records to connect these different representations to the underlying multimodal episodes. The agents are able to find previous experiences by means of contextual cues and to examine the original observations in order to recover details that are missing from their textual descriptions. Through consolidation, the agents convert experience into higher-level knowledge and procedures while still keeping references to the original episodes. In the case of long-horizon Minecraft and computer-use tasks, MFM leads to improved performance with three base models. With GPT-6-Astra, MFM completes \emph{Mine Diamond from Scratch} and reaches 16 out of 24 milestones on the \emph{Ender Dragon} task, as against 12.7 for the best baseline. On CUABench-KiCad it attains a mean reward of 0.440, as compared to 0.400 for the best baseline and 0.267 in the case of no persistent memory.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39044

Catch It All!: Generalizable Dynamic Catching with a Dexterous Hand

Arth Shukla, Kehlani Fay, Huaze Liu, Darin Djapri, Alexiy Buynitsky, Nicklas Hansen

原文摘要

Catching arbitrary objects requires generalization to novel projectiles and real-time adaptation to unpredictable contact dynamics under noisy visual observations. This paper presents Catch It All!, a sim-to-real catching framework for humanoid robots that generalizes and adapts to unseen objects with variable physical properties for underhand and overhand catches, using only one egocentric camera. We learn policies which generalize to unseen objects with varying geometry, deformability, elasticity, and physical properties. Effective grasps which adapt real-time to object poses and shapes are learned through careful grasp reward design. We provide safety-aware curricula which enable safe, high-frequency action execution near hardware limits, including wrist torque limits as low as 5 Nm. We validate our system by deploying zero-shot on a Unitree G1. With only one egocentric camera, Catch It All! successfully performs underhand and overhand catches on 9 unseen objects not seen during training, including low-rebound plushes, semi-deformable toys and chip bags, and rigid-elastic objects with high rebound on contact.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39043

Modular Bayesian Foundation Models: Bayesian time series encoders for OOD detection, routing and forecasting

Mame Diarra Toure, Quang Hung Pham, Mohamed Ryad Zemouri, David Stephens, Luc Vouligny, Martin Gagnon

原文摘要

Industrial systems continuously generate time series that vary in sampling frequency, scale, dynamics, and operating regime. Consequently a single time-series foundation model may not be able to represent all regimes effectively and retraining a large model for every new dataset or regime shift is computationally expensive. Modular foundation models address this issue by reusing specialised encoders trained on diverse datasets and by allowing the expansion of capabilities through the addition of new experts. This approach requires compact experts to minimize storage cost and a proper selection mechanism of experts for unseen data. We propose a modular foundation model framework that combines end-to-end Bayesian encoder training with uncertainty based routing. Using Singular Bayesian Neural Networks (SBNNs), we build a collection of compact Bayesian encoders whose epistemic uncertainty guides label-free selection. At the selected ranks, our Bayesian collection uses 63.8\% less storage than its deterministic counterpart. We evaluate this framework on time-series data through distribution shift, encoder selection, and probabilistic forecasting. Across seven incoming datasets, uncertainty-based routing is competitive with distance-based routing in terms of forecast MSE, while reducing average ECE from 0.086 to 0.072 and average NLL from 0.907 to 0.836. It also increases PICP90 from 82.28\% to 89.03\%. In a separate model-family comparison, we evaluate the SBNN pipeline against full-rank Bayes by Backprop, deep ensembles, and MC Dropout. The SBNN pipeline achieves an average MSE of 0.390, comparable with 0.396 for deep ensembles, 0.389 for MC dropout, and 0.443 for the full-rank Bayesian model. It uses 63.9\% fewer parameters than MC dropout, 81.9\% fewer than the full-rank model, and 92.8\% fewer than the deep ensemble.

stat.ML · cs.LG · stat.AP · stat.CO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39041

Exponential-Nemytskii Neural Operators: Learning Field Evolution Across Step Sizes

Jingye Li, Jinglai Li

原文摘要

In variable-step operator learning, agreement at observed step sizes does not by itself determine predictions at other temporal resolutions. We propose an operator whose step-size dependence is prescribed by a first-order Duhamel approximation of semilinear dynamics. It combines exact linear propagation with a local nonlinear function of the field and its derivatives, allowing evaluation below the observation interval without supervision densely sampled across step sizes. Under suitable conditions and at fixed spatial resolution, we prove that agreement between models at two step sizes controls their prediction differences throughout a prescribed range. We also give conditions for local identifiability of both components from two step sizes, supporting interpretation of the learned structure. A latent extension applies the same construction to learned fields. We evaluate the base model on two synthetic problems and the extension on two real datasets against several baselines.

cs.LG · math.NA

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.19673

InHabit: Scaling 3D Human-Scene Interaction Data With Image Foundation Models

Nikita Kister, Pradyumna YM, István Sárándi, Jiayi Wang, Anna Khoreva, Gerard Pons-Moll

原文摘要

Embodied AI and human-centric 3D scene understanding require data of people meaningfully interacting with diverse environments, yet such data remains scarce. Real-world capture has steadily pushed toward more scenes, but is bound to studios and at most around a hundred scenes, while synthetic datasets scale easily but place people without regard to what the scene affords. We argue that useful human-scene interaction data requires diversity in both scenes and interactions, and we show this with our proposed data engine InHabit. InHabit is a fully automatic engine that renders an existing 3D scene, uses 2D foundation models to propose and depict context-appropriate interactions, and lifts them into SMPL-X bodies, where the known scene geometry makes lifting well-constrained. Applied to Habitat-Matterport3D, InHabit produces InHabitants, which pairs photorealistic images with SMPL-X bodies and complete scene geometry across ~6.6k rooms, about 60x more scenes than the largest captured dataset. We illustrate the quality and value of such large-scale human-scene interaction data through a variety of experiments. In a perceptual study, our interactions are preferred over two prior placement methods in 78% of cases. Across three tasks - 1) contact estimation, 2) human-scene reconstruction, and 3) generating humans in scenes - training with InHabitants outperforms training on synthetic data with exact ground truth (BEDLAM), motion-capture data (TRUMANS), and real RGB-D captures (PROX); e.g., GRAFT trained on InHabitants reaches a contact F1 of 0.594 on PROX, versus 0.492 when trained on PROX itself. Project page: https://virtualhumans.mpi-inf.mpg.de/inhabit/

cs.CV

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