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版本更新 · v2arXiv:2605.30326

RoboWits: Unexpected Challenges for Robotic Creative Problem Solving

Chunru Lin, Hongxin Zhang, Fenghao Yu, Zhehuan Chen, Thomas L. Griffiths, Yejin Choi, David Held, Chuang Gan

原文摘要

The ability to reason, adapt, and creatively solve problems under unexpected challenges is essential for robots operating in real-world environments. However, current robotic benchmarks primarily emphasize skill-level execution and provide limited insight into such cognitive reasoning capabilities. We introduce RoboWits, a bi-manual robotic benchmark designed to systematically evaluate cognitive reasoning, creative tool use, and robustness to unexpected conditions. To enable scalable construction of high-quality reasoning-centric unexpected scenarios, we propose an automated task generation pipeline formulated as a multi-agent cooperative framework, comprising agents for seed task generation and verification, metric generation, scene generation, and task mutation. Using the pipeline, we curated 30 diverse seed tasks and 200 tasks with mutations and graded difficulty across geometry, material, and assembly-based reasoning. We benchmark popular robot policies, pre-trained VLAs, a zero-shot policy built on a frontier VLM, and oracle-state planners. Our results reveal a significant performance gap: while pre-trained VLAs exhibit preliminary success on seed tasks after fine-tuning, they struggle to perform on mutated tasks, implying their brittleness in manipulation tasks requiring reasoning, strategy adaptation, and robustness to deceptive or constrained environments. A zero-shot policy built on GPT-6 Astra outperforms all fine-tuned VLAs, yet still loses about a fifth of its success on mutated tasks, showing that stronger foundation models alone do not close the robustness gap. See our project page https://umass-embodied-agi.github.io/RoboWits/ for videos.

cs.RO · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.13389

Logit-Conditioned Diffusion Decoding for Frozen Discrete-Token VLMs

Ji Woo Hong, Gwanhyeong Koo, Hee Suk Yoon, Eunseop Yoon, SooHwan Eom, Qi Dai, Chong Luo, Chang D. Yoo

原文摘要

Pre-trained discrete-token VLMs generate images by sampling sequences of codebook indices, but their visual fidelity is bounded by the VQ-VAE decoding stage. Prior work addresses this bottleneck by replacing the discrete tokenizer with continuous or hybrid visual token representations, or by integrating diffusion into the generation pipeline; both require foundation-scale VLM retraining or realignment. We show this is unnecessary: before each token is sampled, discrete-token VLMs already compute a full logit distribution over the codebook, but standard decoding discards it after selecting a single index. Under Otsu's threshold, the pre-sampling logits of capable VLMs assign statistically meaningful probability to at least two codebook entries per token, and recovering this distribution can substantially close the fidelity gap. We propose DCDD, a post-hoc framework that conditions a diffusion decoder on this discarded distribution while leaving the VLM frozen. At inference, DCDD converts the VLM's pre-sampling logits into distribution-weighted code vectors; during training, it uses VQ-VAE encoder-derived proxy logits calibrated to match the distributional statistics of the VLM's inference-time logits. The decoder maps these representations to high-fidelity images as an alternative to the native VQ-VAE decoder. Trained on ImageNet-1K for 50K steps, DCDD reduces reconstruction FID by 74% and Janus-Pro 7B's generation FID on MJHQ-30K by 34%, while semantic alignment across GenEval, DPG-Bench, and WISE remain consistent. Controlled comparisons with diffusion models from the same backbone family, applied either as an image-to-image refiner or as an identically trained decoder on the sampled codes, confirm that the gain stems from conditioning on the VLM's pre-sampling logit distribution rather than from the diffusion model itself.

cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.03698

Training LLM Agents to Automate LLM Post-Training with RSI-Dojo

Shuzheng Gao, Zhaojian Yu, Penghao Yin, Yuling Shi, Kai Cai, Shilin He, Michael R. Lyu

原文摘要

Recursive Self-Improvement (RSI) aims to enable AI systems to autonomously build and improve AI systems themselves, including models, harnesses, and underlying infrastructure. Within this broader agenda, a central capability is enabling agents to train and improve base models. However, existing studies on automated model training primarily focused on workflow- or harness-level optimizations, rather than internalizing the ability to conduct post-training into the model itself. Developing this capability requires executable model training environments that can produce high-quality trajectories demonstrating how to select data, train models, diagnose failures, and iteratively improve results. In this paper, we introduce RSI-Dojo, a collection of executable model training environments designed to teach LLM agents to conduct post-training. To construct RSI-Dojo, we develop RSI-Craft, an automatic pipeline that converts human research artifacts into executable model training environments. RSI-Craft automatically collects research papers from top venues, instantiates datasets and metrics into a unified interface, and reproduces evaluation dependencies. It then filters tasks by data size, execution reliability, and discriminability, yielding RSI-Dojo with 281 seed model training tasks. Building on RSI-Dojo, we employ a trajectory collection method with an improved harness to collect high-quality model-training trajectories from frontier models, then retain examples through rubric-based and outcome-based filtering. We fine-tune Qwen3.5-9B and Qwen3.6-35B-A3B on these trajectories. The resulting RSI-Dojo-9B and RSI-Dojo-35B-A3B improve over their base models by 8.74 and 16.87 PostTrainBench points, respectively. RSI-Dojo-35B-A3B reaches 29.95 on PostTrainBench and 53.29 on PostTrainBench-Lite, matching the performance of frontier models such as GLM-5.2 and GPT 5.5.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2504.17410

Bias-Eliminated PnP for Stereo Visual Odometry: Provably Consistent and Large-Scale Localization

Guangyang Zeng, Yuan Shen, Ziyang Hong, Yuze Hong, Viorela Ila, Guodong Shi, Junfeng Wu

原文摘要

In this paper, we first present a bias-eliminated weighted (Bias-Eli-W) perspective-n-point (PnP) estimator for stereo visual odometry (VO) with provable consistency. Specifically, leveraging statistical theory, we develop an asymptotically unbiased and $\sqrt {n}$-consistent PnP estimator that accounts for varying 3D triangulation uncertainties, ensuring that the relative pose estimate converges to the ground truth as the number of features increases. Next, on the stereo VO pipeline side, we propose a framework that continuously triangulates contemporary features for tracking new frames, effectively decoupling temporal dependencies between pose and 3D point errors. We integrate the Bias-Eli-W PnP estimator into the proposed stereo VO pipeline, creating a synergistic effect that enhances the suppression of pose estimation errors. We validate the performance of our method on the KITTI and Oxford RobotCar datasets. Experimental results demonstrate that our method: 1) achieves significant improvements in both relative pose error and absolute trajectory error in large-scale environments; 2) provides reliable localization under erratic and unpredictable robot motions. The successful implementation of the Bias-Eli-W PnP in stereo VO indicates the importance of information screening in robotic estimation tasks with high-uncertainty measurements, shedding light on diverse applications where PnP is a key ingredient.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2610.02902

LUMOS: Tracing Parametric Knowledge from Training Data to Behavioral Outputs in LLMs

Seoyeon Ye, Gayoung Kim, Jiyoung Hong, Sookyung Kim, Hyunsoo Cho

原文摘要

Current analyses of LLMs' parametric knowledge are largely output-centric, drawing conclusions about what a model knows without verifying what it was actually trained on. This leaves fundamental questions, such as whether a correct response reflects genuine generalization or rote memorization, grounded in speculation rather than evidence. To resolve these ambiguities, we introduce LUMOS, a diagnostic framework that traces knowledge across training-data exposure, internal representations, and behavioral outputs, leveraging OLMo 2 with its fully transparent training corpus. By grounding analysis in verified exposure, we reveal that models internally encode rare facts with high separability (84%) yet fail to express them behaviorally (54%), though this retrieval gap narrows with scale. Furthermore, when models are asked to self-reflect on their own answers, they perform reliably on trained content (83%) but drop to random-baseline levels (49%) on unseen content. This collapse persists even under chain-of-thought prompting, which inflates confidence signals rather than improving calibration. Collectively, these findings demonstrate that incorporating the training-data axis into LLM evaluation transforms speculative diagnoses into verifiable claims, and we advocate that this axis should be a standard component of knowledge assessment in LLMs.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.06274

When the Agent Edits Its Own Limits: Evaluating a Write-Time Floor Against Prompt Prohibitions

Sajib Hossain, Moeen Uddin Mahmud

原文摘要

Many agent runtimes give the agent a tool for editing its own configuration. Some settings grant abilities, such as enabling a tool. Others set the agent's limits: where it may write, who may message it, which address it listens on, how callers authenticate, and the gate that blocks risky writes. We measure how often models widen those limits while handling ordinary requests, and how well two kinds of defence hold: a prohibition in the system prompt, in three wordings, and a check at the point of writing that we call a containment floor. Every case records whether a protected write was attempted and whether it was saved, read from the tool's arguments and from the file rather than from the model's reply. With no defence, a frontier model saved a protected value on 25 of 72 requests, often when the request never named the field. A prompt that listed the protected field names stopped every request that used those names and did not reduce writes on requests that only described the goal; a prompt that described the forbidden effects did the reverse. With the floor, 0 of 167 protected writes were saved while 65 were attempted. A single-pass pilot on two further models shows the same pattern. We draw four lessons for evaluating agent safeguards: count attempts beside outcomes, test a prompt defence with requests written in a different vocabulary from the prompt, check that a zero means the safeguard fired, and log what the harness sends to the model, because a request-ordering defect in our own runtime was invisible in the transcripts and was found again, independently, by a later benchmark.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2509.05009

Computing the Elementary Symmetric Polynomials in Positive Characteristics

Ian Orzel

原文摘要

We first extend the results of Chatterjee, Kumar, Shi, Volk (Computational Complexity 2022) by showing that the degree-$d$ elementary symmetric polynomials in $n$ variables have arithmetic formula lower bounds of $Ω(d(n-d))$ over fields of positive characteristic. This is done by characterizing the dimension of the singular locus of the elementary symmetric polynomials in positive characteristic fields (parts of the characterization only hold over infinite fields). Then, we show that the results of the universality of border fan-in-two $ΣΠΣ$-circuits from Kumar (ACM Trans. Comput. Theory 2020) over zero-characteristic fields do not extend to fields of positive characteristic. This is done through studying linear projections of the elementary symmetric polynomial as studied by Shpilka (Journal of Computer and System Sciences 2002).

cs.CC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.11559

Multi-Turn Agent Self-Distillation with Compressed Hindsight

Haoran Liu, Yuwei Zhang, Xiyao Li, Bohan Lyu, Jingbo Shang

原文摘要

In reinforcement learning for multi-turn agents, a sparse outcome reward makes credit assignment across intermediate tool calls difficult. On-policy self-distillation eases credit assignment by having the model teach itself with dense supervision: the model with privileged information (the teacher) scores each token generated by the model without it (the student). However, it remains unclear what forms of feedback are most effective in multi-turn agentic settings. We find that the natural multi-turn variant fails: when the teacher is given a complete successful demonstration at every turn, training makes the agent worse than the untrained model. The teacher ends up rewarding the student for copying the demonstration, even when a copied action is not right in the student's context. We propose HINT, in which a reflector, by default the policy itself, reads the completed trajectory and writes short turn-level feedback. The teacher then sees what went wrong at each turn, not the actions that followed. On Tau-Bench and WebShop, HINT matches GRPO with one-eighth as many rollouts.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.28114

Agent Personas Shift Allocation Decisions About People: What Matters Is Whether the Two Strings Match

Messi H. J. Lee

原文摘要

Language models are increasingly used to make decisions about people, such as whom to interview, which insurance claims to approve and which patients to see first. Research on bias in these decisions has focused on whether a person's race or gender changes the outcome. However, developers also write text that the model reads before every decision, including a persona that gives the model a role and often names a project, team or office. Whether such text affects whom a model selects remains largely unexamined. We asked what happens when a string in the persona also appears in one candidate's record. Across four kinds of decision, fourteen publicly released models and 328,480 choices between two candidates, instruction-tuned models, the kind used in chat assistants, selected the candidate whose record repeated the persona's string in 71.3% of decisions between equally qualified candidates, compared with 50.1% when no string was shared. When that candidate was one step less qualified, the rates were 31.5% and 11.7%. A made-up project code was enough. The effect persisted when the record labelled the string a conflict of interest, even in models that otherwise selected such a candidate less often. When the shared string was the candidate's race, a similar shift appeared. Bias audits built around protected attributes can at most show the resulting gaps between groups, not their source.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.18982

SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution

Xiachong Feng, Yi Jiang, Xiaocheng Feng, Deyi Yin, Libo Qin, Yangfan Ye, Lei Huang, Weitao Ma 等 12 位作者

原文摘要

Social intelligence, the ability to navigate complex interpersonal interactions, presents a fundamental challenge for language agents. Training such agents via reinforcement learning requires solving the credit assignment problem: determining how individual utterances contribute to multi-turn dialogue outcomes. Existing approaches directly employ language models to distribute episode-level rewards, yielding attributions that are retrospective and lack theoretical grounding. We propose SAVOIR (ShApley Value fOr SocIal RL), a novel principled framework grounded in cooperative game theory. Our approach combines two complementary principles: expected utility shifts evaluation from retrospective attribution to prospective valuation, capturing an utterance's strategic potential for enabling favorable future trajectories; Shapley values ensure fair credit distribution with axiomatic guarantees of efficiency, symmetry, and marginality. Experiments on the SOTOPIA benchmark demonstrate that SAVOIR achieves new state-of-the-art performance across all evaluation settings, with our 7B model matching or exceeding proprietary models including GPT-4o and Claude-3.5-Sonnet. Notably, even large reasoning models consistently underperform, suggesting social intelligence requires qualitatively different capabilities than analytical reasoning.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v7arXiv:2602.14612

Event-Grounded Question Answering over Long Audio via Structured Retrieval

Kartik Hegde, Arvind Krishna Sridhar, Naveen Vakada, Yinyi Guo, Erik Visser

原文摘要

Answering natural-language questions over multi-hour audio requires reliable event recognition, temporal grounding, and efficient retrieval. We present LA-RAG (Long Audio Retrieval-Augmented Generation), a structured framework that converts audio into timestamped event records, stores them in an event database, and answers questions using intent-aware retrieval and LLM-based generation. LA-RAG supports two deployment settings: offline grounding mode, in which long recordings are pre-indexed for low-latency querying, and inference-time grounding mode, which performs query-conditioned grounding over shorter, open-ended clips. We evaluate LA-RAG on two synthetic 24-hour benchmarks, Home-IoT and Industrial-IoT, and on CASTELLA-QA and SpotSound-Bench-QA, which are QA versions of two public audio temporal grounding benchmarks that we construct. The results demonstrate effective long-audio question answering with low query-time latency after grounding and indexing. They also reveal a substantial gap between event detection and temporal localization in current large audio-language models. Explicit timestamped grounding consistently improves temporal localization while maintaining competitive detection accuracy. These findings establish structured grounding and retrieval as a practical complement to generative audio-language models for deployment-oriented long-audio understanding.

eess.AS · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.07891

Beyond Retargeting: Low-Latency and Robust Humanoid Whole-Body Teleoperation with Learned Atomic Motion Primitives

Xiayan Xu, Jiyu Yu, Xingzhou Chen, Siyi Qian, Zongyu Ma, Lilu Liu, Ling Shi, Haodong Zhang

原文摘要

Humanoid whole-body teleoperation translates human motion into stable robot behavior in real time. Existing systems typically rely on online motion retargeting to bridge human--robot morphological differences, but this process adds latency and can produce physically infeasible targets. Meanwhile, diverse, noisy, and partial human-motion observations often fall outside the training distribution, potentially causing unstable robot behavior. We propose a retargeting-free policy that maps raw human motion directly to robot joint commands in a single forward pass, eliminating online kinematic adaptation. To improve robustness, we learn a codebook of full-body motion primitives that projects out-of-distribution observations onto plausible motion prototypes and recovers full-body motion from partial inputs. Experiments on a Unitree~G1 in simulation and on hardware, using virtual reality, optical mocap, text-to-motion generation, and monocular video inputs, show that our method outperforms baselines in latency and robustness.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.11594

PointForward: Feedforward Driving Reconstruction through Point-Aligned Representations

Cheng Chi, Xianqi Wang, Hongcheng Luo, Mingfei Tu, Gangwei Xu, Zehan Zhang, Bing Wang, Guang Chen 等 12 位作者

原文摘要

High-fidelity reconstruction of driving scenes is crucial for autonomous driving. While recent feedforward 3D Gaussian Splatting (3DGS) methods enable fast reconstruction, their per-pixel Gaussian prediction paradigm often suffers from multi-view inconsistency and layering artifacts. Moreover, existing methods often model dynamic instances via dense flow prediction, which lacks explicit cross-view correspondence and instance-level consistency. In this paper, we propose PointForward, a feedforward driving reconstruction framework through point-aligned representations. Unlike pixel-aligned methods, we initialize sparse 3D queries in world space and aggregate multi-view image information via spatial-temporal fusion onto these queries, enforcing explicit cross-view consistency in a single feedforward pass. To handle scene dynamics, we introduce scene graphs that explicitly organize moving instances during reconstruction. By leveraging 3D bounding boxes, our method enables instance-level motion propagation and temporally consistent dynamic representations. Extensive experiments show that PointForward achieves state-of-the-art performance on large-scale driving benchmarks.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2611.02686

Discovering Semantic Granularity in Images through Clustering of Pretrained Visual Representations

Ying Dai

原文摘要

Semantic granularity, which refers to the levels at which visual representations organize image content into meaningful entities, is fundamental to image understanding. However, whether pretrained visual representations inherently encode semantic structures at multiple granularities remains poorly understood. This paper presents a representation-driven approach for discovering semantically meaningful image regions at different granularities through clustering of pretrained visual representations without task-specific training. Specifically, we concatenate feature maps from appropriately deep layers of a pretrained visual model and apply a singular value decomposition (SVD)-based projection to derive a pixel-wise principal representation map (PRM) tensor. The resulting PRM tensor captures latent semantic structures embedded in pretrained visual representations, making them accessible for subsequent semantic clustering. We then introduce the semantic continuity ratio (SCR) and analyze the signal-to-noise ratio (SNR) of SCR profiles to assess the suitability of PRM tensors for semantic granularity discovery. Our results indicate that PRM tensors exhibiting a higher proportion of relatively high SNR values over an initial range of cluster numbers tend to yield more semantically meaningful regions across multiple granularities. Furthermore, once a suitable PRM tensor is identified through SNR-profile analysis, pixel-wise hierarchical clustering enables the level of semantic granularity to be controlled by varying the number of clusters. We evaluate the proposed approach on the COCO-2017 and VOC-2012 validation datasets using Hungarian-matched intersection over union (HIoU). The results suggest competitive segmentation performance among the compared methods, supporting the feasibility of the proposed approach.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2505.16409

Retrieval as Exploration: Retriever-Free Retrieval via Corpus-Traversing MCTS

Chaeeun Kim, Seungone Kim

原文摘要

Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in multi-step reasoning and calling search tools at appropriate steps. However, existing retrieval-augmented reasoning approaches still rely on separate retrieval models, limiting LRMs to deciding when to retrieve and how to formulate queries rather than leveraging their pretrained knowledge and reasoning capabilities for retrieval itself. This structural separation introduces a representation bottleneck, as retrieval relies on separately learned representations that may obscure fine-grained distinctions needed for reasoning. Moreover, these approaches commonly employ fixed top-k retrieval, which can introduce superfluous documents and increase retrieval noise and context overhead. To address this, we reformulate retrieval from query-document relevance matching into the exploration of answer-containing paths within the corpus, and propose FREESON (Retriever-FREE Retrieval-Augmented ReaSONing), a framework that enables LRMs to directly navigate the original corpus text to locate information that satisfies their specific needs, serving as both the reasoner and the retriever. Specifically, we introduce CT-MCTS (Corpus-Traversing Monte Carlo Tree Search), a retrieval-specialized variant of Monte Carlo Tree Search (MCTS). CT-MCTS iteratively expands and evaluates exploration paths, starting from multiple promising entry points across the corpus. This allows LRMs to selectively reference specific segments identified during traversal, instead of fetching a fixed top-k set. Across nine benchmarks, FREESON improves average EM and F1 by 15 points over four retriever-based baselines on five general-domain QA tasks, matching the strongest baseline. It also gains 12 points on fact-checking and 13 points on medical QA under zero-shot transfer, performing on par with the much larger MedGemma-27B.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.06487

Measuring the collaboration, not the agent

Xinxiao Li, Eisei Nakahara

原文摘要

Benchmarks for visualization agents score the agent alone; the human only supplies reference answers. For high-dimensional data this misses what matters: the knowledge of which variables count is held by on-site experts, not by the model, so the useful question is how well an analyst and an agent do together. We study one specific form of collaboration, the agent proposes a view, the analyst accepts, rejects, or modifies it, and those judgments are kept for later sessions, and propose five measures for it: complementarity, intervention efficiency, appropriate reliance, knowledge retention, and agency. The first four are computed from interaction logs alone. We test the measures, not people: in a testbed for parallel-coordinates design on public manufacturing data (UCI SECOM), with a simulated analyst whose behavior we control, all four are computable and respond to the controlled differences. Whether they reveal anything about real analysts is the next step; we release the testbed for it.

cs.HC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.37022

Physics-Informed Multi-Agent Coordination for Hospital Patient Flow Optimization

Guoqing Zhang, Rafik Hadfi, Takayuki Ito

原文摘要

Efficient patient flow coordination across autonomous hospital departments is critical for mitigating overcrowding and balancing resource utilization. While classical queueing theory, specifically open Baskett--Chandy--Muntz--Palacios (BCMP) networks, provides an interpretable mathematical topology for healthcare operations, analytical models rely on stationary assumptions and fixed routing matrices that degrade under state-dependent real-world dynamics. Conversely, centralized reinforcement learning approaches struggle to accommodate the decentralized structure of hospital governance, where individual clinical departments function with localized observations, heterogeneous resources, and divergent operational objectives. In this paper, we present a Multi-Agent Systems (MAS) framework titled \emph{Physics-Informed Multi-Agent Coordination}, which embeds empirically calibrated BCMP queueing topologies as physical priors within a decentralized multi-agent reinforcement learning architecture. Formulated as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) under coupled resource constraints, our method enables autonomous departmental agents to cooperatively negotiate patient routing and dynamic service scaling. To mitigate environmental non-stationarity without inducing excessive communication overhead, agents exchange localized action fingerprints along network edges and optimize a spatially decomposed reward structure. Empirical evaluations driven by real-world MIMIC-IV patient trajectories indicate that this cooperative multi-agent approach substantially reduces cumulative system delay compared to static Markovian approximations, heuristic dispatching, and independent multi-agent baselines, while maintaining clinical safety constraints.

cs.AI · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.21954

Are Multilingual Models Actually Improving? Revisiting Cross-Lingual Transfer

Prasoon Bajpai, Eleftheria Briakou, Colin Cherry, Preethi Jyothi, Vihari Piratla

原文摘要

Cross-lingual transfer is a model's ability to generalize capabilities from well-represented source languages to under-represented target languages. Existing measures of a model's transfer strength conflate improvements in transfer with general improvements to accuracy in the source language. We advocate for an alternate metric that reliably captures transfer strength called Hardness Adjusted Transfer (HAT) Score, and use it to derive multiple insights on factors influencing transfer strength. Our analysis across twenty diverse language models and three popular mainstream multilingual benchmarks argues that 1) transfer in small models is not broken, 2) we are making slower than expected progress in cross-lingual transfer with model size, and 3) we have made clear progress over time.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.08477

EgoGeneralist: Unifying Egocentric Action-Video Tasks through Video-Native Modeling

Yanlin Jin, Bowen Yang, Cheng Zhang, Yifan Yang, Zhe Li, Jianfei Cai, Kai Zhu

原文摘要

Modern video models possess strong visual priors, yet do not natively represent human action in metric 3D space. We present EgoGeneralist, which adapts pretrained video generators to unify action-conditioned video generation, pose estimation, and joint action-video generation for egocentric manipulation. Our role-adaptive action renderer provides video-native representations for multidirectional action-video learning, making metric motion usable as both a spatial control signal and a recoverable generation target. Cross-stream action-video coupling establishes close correspondence between metric motion and visual dynamics through lightweight adaptation while preserving the pretrained visual prior. Together, these designs establish a generalist framework spanning six action-video tasks. With only narrow-domain data, EgoGeneralist generalizes to diverse unseen scenes and interactions with effective action control and visual quality, bridging pretrained visual knowledge and metric 3D human motion.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.22212

A Channel-Boosted Multi-Agent System with Iterative Consultation for Document Sensitivity Classification

Aleesha Zainab, Asifullah Khan, Muhammad Ahmed Khalid, Faheem Ullah Khan

原文摘要

Organizations in critical national infrastructure sectors must assess heterogeneous documents for sensitivity before routing or storage. Manual assessment is slow, inconsistent, and unscalable. Extending our prior leakage-controlled benchmark, BERT established the top single-encoder baseline (89.14% accuracy, 89.33% F1-score under 5-fold cross-validation on the Strategic 16K corpus). However, transformer baselines suffer from a structural limitation: fixed input length truncation discards evidence beyond the retained window-precisely where sensitive cables tend to be longest. We present Channel-Boosted MAS (CB-MAS) and instantiate it as IC-MAS (Iterative Consultation Multi-Agent System) to solve this without long-context computational costs. A Channel Critic Agent learns document-adaptive trust weights governing Gated Channel Boosting between two first-window encoders, while paired Consultation Agents iteratively exchange belief states to reconcile evidence from the beginning and end of long documents. IC-MAS holds computation constant regardless of document length by reconciling fixed windows in a compact representation space. Ablation studies show critic-controlled Channel Boosting provides the bulk of accuracy gains, while consultation recovers recall without precision collapse. Critic-Controlled Gated Channel Boosting with Max-Pool fusion and Blackboard Adaptive Consultation achieves 90.72% accuracy, 91.23% F1-score, 92.01% sensitive recall, and 90.46% sensitive precision, using about 54% less average computation than a fixed-round baseline. Gains over the single-encoder baseline are statistically significant (McNemar's test, p less than 0.000001; paired t-test). We include LIME/SHAP explainability, multi-agent evaluation, and an honest accounting of limitations.

cs.CL · cs.LG · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.20135

Frequency-Aware Flow Matching for Continuous and Consistent Robotic Action Generation

Jianing Guo, Fangzheng Chen, Zihao Mao, Wong Lik Hang Kenny, Zhenhong Wu, Yu Li, Yishuai Cai, Yuanpei Chen 等 15 位作者

原文摘要

Flow matching has emerged as a standard paradigm for robotic manipulation owing to its strong expressive power for modelling complex, multimodal action distributions, alongside similar approaches like diffusion policy. However, existing methods rely on discretized action chunks, making them brittle to demonstrations collected at heterogeneous control frequencies and prone to temporally inconsistent actions that degrade control stability. In this paper, we propose Frequency-Aware Flow Matching (FAFM), which outputs continuous, temporally consistent actions. To handle heterogeneous frequency input, we transform discrete action sequences into the frequency domain with the discrete cosine transform (DCT), perform flow matching over the resulting coefficients, and reconstruct continuous actions via cosine basis expansion. To generate temporally consistent actions, we regularize the first-order temporal derivative to promote smooth actions. This corresponds to a Sobolev-type constraint that suppresses high-frequency errors and discourages abrupt action changes. Our FAFM is simple, introduces no additional network parameters and applies to standalone flow-matching policies and vision-language action models. Across synthetic toy benchmark, obstacle avoidance, LapGym, and LIBERO, FAFM improves success rates, multimodal expressivity, motion smoothness, convergence speed, robustness to mechanical bias and mixed-frequency input. These gains are consistent when deployed on a real-world Franka robot. Code available at https://github.com/gakakulicc/FAFM.

cs.RO · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.18309

Alignment Dynamics in LLM Fine-Tuning

Yuhan Huang, Huanran Chen, Yinpeng Dong

原文摘要

Although Large Language Models (LLMs) achieve strong alignment through supervised fine-tuning and reinforcement learning from human feedback, the alignment is often fragile under subsequent fine-tuning. Existing explanations either attribute alignment fragility to gradient geometry or characterize it as a distributional shift in model outputs, yet few provide a unified account that bridges parameter-space learning dynamics with function-space alignment behavior during fine-tuning. In this work, we introduce a tractable alignment score and derive its closed-form update during fine-tuning, yielding a unified framework for alignment dynamics. Our analysis decomposes alignment updates into two competing components: a Rebound Force, governed jointly by the current alignment state and the narrowness of model distribution, and a Driving Force, determined by how the training distribution aligns with outcome-conditioned posteriors over aligned and non-aligned completions. This decomposition explains why prior alignment can be reversed by later fine-tuning and why narrower posterior structure strengthens such reversal. Moreover, our framework predicts a Rehearsal Priming Effect: prior alignment leaves a latent posterior imprint that amplifies the effective Driving Force upon re-exposure, leading to faster re-alignment. We validate these predictions across safety alignment, emergent misalignment, and sentiment settings, demonstrating consistent alignment reversal and accelerated re-alignment under re-exposure. In addition, controlled experiments in safety alignment confirm the predicted dependence of rebound strength on posterior narrowness. Together, these results provide a unified dynamical perspective on how alignment is disrupted and reactivated during LLM fine-tuning.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.14527

DNA-MGC+: A versatile codec for reliable and resource-efficient data storage on synthetic DNA

Ramy Khabbaz, Jérémy Mateos, Marc Antonini, Serge Kas Hanna

原文摘要

The biochemical processes underlying DNA data storage, including synthesis, amplification, and sequencing, are inherently noisy, leading to insertion, deletion, and substitution errors as well as sequence dropouts that challenge reliable data retrieval. Here we introduce DNA-MGC+, a DNA storage codec designed for reliable and resource-efficient retrieval under diverse operating conditions. We evaluate DNA-MGC+ across various in silico and in vitro settings. In comparison with selected representative codecs, the results show that DNA-MGC+ consistently improves key performance metrics under explicit reliability constraints, including required sequencing depth, read cost, decoding time, storage density, and error-correction capability. In silico experiments show that DNA-MGC+ supports reliable decoding at error rates up to 24% for a 15-KB file, while separate scalability experiments demonstrate reliable decoding of files up to 200 MB at a 5% error rate. In vitro experiments involving a 24-KB file and electrochemical synthesis show that different DNA-MGC+ configurations achieve minimum sequencing depths below 3x and read costs below 3.5 nts/bit under both Illumina and Nanopore sequencing.

cs.IT · cs.ET

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.08136

How Far Can Untrained Neural Networks Go in Anomaly Detection? From Tables to Pixels

Hyojun Yi, Geunyoung Kim, Hogeon Seo, Seunghyoung Ryu

原文摘要

Anomaly detection (AD) is often framed as a representation-learning problem, where strong performance is expected to require learned features or AD-specific optimization. However, this view can overlook the intrinsic separability of anomalies from normal references, even in the absence of learned representations. We therefore investigate how far untrained neural networks can go in anomaly detection. To this end, we propose PRISM, a framework combining fixed random nonlinear features with normal-reference Mahalanobis scoring for tabular detection and dense visual localization. Across 47 tabular datasets in ADBench, PRISM-T achieves the highest mean AUROC (86.4%) and the second-highest mean AUPRC (65.9%) among the compared methods. On MVTec AD and VisA, covering 27 visual categories, PRISM-V achieves mean image-level and pixel-level AUROCs of 90.4% and 96.4%, respectively. Notably, both models achieve these results without neural weight optimization or pretrained representations. A theoretical analysis further characterizes how nonlinear transformations affect the mean and covariance contributions to the Mahalanobis score gap, while empirical analyses examine the resulting score separability. These findings establish PRISM as both a competitive anomaly detector and a prior-to-learning baseline for assessing the added value of learned representations in anomaly detection.

cs.LG · cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.16138

Closing the Approximation Gap in Simulation-free Latent SDEs

Henry D. Smith, Brian L. Trippe, Scott W. Linderman

原文摘要

Recovering dynamical systems from noisy observations is a recurring challenge across scientific domains, including neuroscience and physics. Latent stochastic differential equations (SDEs) address this by modeling the system as an unobserved state that evolves according to a learnable SDE and generates the observations. Variational inference (VI) provides a tractable objective for fitting latent SDEs. Traditional VI algorithms evaluate this objective by numerical simulation over a time discretization, trading fidelity for computational cost. A recent class of algorithms, simulation-free VI, sidesteps this tradeoff by parameterizing the posterior through its instantaneous marginals rather than its drift. In this work, we show that the efficiency of existing simulation-free VI algorithms comes at a price: their parameterizations restrict the approximate posterior to a subset of the SDEs available to simulation-based methods, degrading posterior inference and parameter learning. We propose Helmholtz-SDE, a simulation-free VI algorithm that closes this gap by optimizing over path laws compatible with a prescribed collection of marginals. Helmholtz-SDE recovers dynamics more faithfully than prior simulation-free methods, with the largest gains under high posterior uncertainty. It further matches the performance of simulation-based VI at a fraction of the runtime. Code is available at https://github.com/lindermanlab/helmholtz-sde .

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.00580

Scaling Science: Toward a Chain Reaction of Discovery

Yuheng Ding

原文摘要

Scientific discovery advances when evidence from different investigations is brought into contact. Yet paper-centric communication leaves experimental results fragmented, delayed, or unreported, creating a "scientific memory wall" between growing computational capabilities and the evidence accessible to AI systems. Breaking this wall requires a paradigm shift in AI for Science: moving from passively mining questions out of existing datasets to actively generating and recombining experimental data around unresolved scientific questions. In a network of intelligent laboratories, a failed or overlooked experiment in one domain could trigger a hypothesis in another, whose testing generates fresh evidence that stimulates further investigations. To enable these evidence-driven chain reactions, we propose Evolving Scientific Intelligence (ESI), an architectural blueprint that connects shared scientific memory with distributed experimental loops. ESI preserves experimental records -- including negative and anomalous results -- in computable representations, allowing models to recombine evidence across domains into testable hypotheses. Rather than following fixed screening pipelines, the system jointly selects candidates and evaluation methods according to scientific value, reliability, and cost, directing distributed laboratories to produce the exact evidence needed next. Their physical feedback revises both hypotheses and evaluators, while slower meta-control reassesses research objectives and decision criteria to guard against self-reinforcing errors and misguided optimization. We call for an Information Manhattan Project to build this infrastructure for scaling science through experimentally grounded chains of discovery.

cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2610.41132

When Disclosure Is Compulsory: Evaluating Ride-Hailing Rebalancing Policies with Self-Interested LLM Driver Agents

Xinyu Wang, Andres Fielbaum

原文摘要

The EU Platform Work Directive, to be transposed by December 2026, requires ride-hailing platforms to state in plain language the main parameters of their automated decisions and how a worker's behaviour affects them. Such rules are exactly what a large language model (LLM) can read, so drivers will be able to let one decide for them. We compare two main families of rebalancing policy once their rules are public: a reactive policy (R) that sends idle drivers to cover requests just lost, and an anticipatory policy (A) that plans moves on a demand forecast. Each driver is an LLM agent maximizing its own net income, given no rules (N) or the rules at the directive's level of detail (L). We test two LLMs, Claude Haiku 5.5 and Jev, which understand the rules equally well, in a three-zone model and in Manhattan. With the rules disclosed, drivers follow the platform more closely: in Manhattan, the share of relocation offers they decline falls from about four in five to one in four under both policies. Yet the effects differ by policy. For the platform, disclosure costs R 3.7% of its profit in Manhattan, as R now pays for moves drivers used to make for free, while A's profit is unchanged; the two policies, which tie on profit under full compliance, end 6.1% apart. For drivers, disclosure raises income in the small model under both policies and LLMs; for users, it raises the service rate there by 10 to 18 percentage points. In Manhattan, self-interested drivers also make A a win-win-win against full compliance, raising driver income, platform profit and service rate by 10 to 12% each. Rebalancing policies that perform equally well under full compliance can thus show opposite results under mandatory disclosure. The study offers a framework to evaluate such policies under the new requirement and is, to our knowledge, the first to do so with LLM agents.

cs.MA · cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2510.09206

A reverse entropy power inequality for i.i.d. log-concave random variables

Zhen Fu, Jiange Li

原文摘要

We show that $h_\infty(X+Y)\leq h_\infty(Z+W)$, where $X, Y$ are independent log-concave random variables, and $Z, W$ are exponential random variables having the same respective $\infty$-Rényi entropies. Analogs for integer-valued monotone log-concave random variables are also obtained. Our main tools are decreasing rearrangement, majorization, and the change of measure.

math.PR · cs.IT · math.FA · math.MG

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2606.03021

Hint-Guided Diversified Policy Optimization for LLM Reasoning

Zhiyu Cao, Kaixin Wu, Mingjie Zhong, Peifeng Li, Can Ye, Qiaoming Zhu

原文摘要

Recent developments in Large Language Models (LLMs) have showcased impressive reasoning capabilities, with Reinforcement Learning with Verifiable Rewards (RLVR) being a promising enhancement strategy. However, existing reward mechanisms are constrained to the outcome-level correctness and lack explicit signals to guide the model to consider diverse solutions. In contrast, human problem solving typically involves evaluating multiple potential approaches and selecting the most reliable solution, a cognitive process that current RLVR frameworks do not explicitly incentivize. Inspired by this, we propose Hint-Guided Diversified Policy Optimization (HDPO), allowing the model to first list all potential candidate solution outlines as hints and then select the most reliable one for further reasoning. HDPO comprises two stages of Cold Start for Structured Reasoning and Hint-Guided Diversified Reinforcement Learning to incentivize the model to generate diverse and reliable solutions following the ``propose-select-think'' trajectory. Experimental results show that HDPO effectively boosts LLM reasoning and enhances the diversity of candidate solutions as well as the LLM's ability to identify reliable solutions.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.01462

An Enigma of Artificial Reason: Investigating the Production-Evaluation Gap in Large Reasoning Models

Mingzhong Sun, Teresa Yeo, Armando Solar-Lezama, Tan Zhi-Xuan

原文摘要

Studies of human reasoning have shown that people are typically stronger at evaluating reasoning than producing it from scratch. In contrast, large reasoning models (LRMs) are trained to excel at producing long chains of reasoning to solve complex problems. How then do LRMs perform at evaluating reasons? We investigate this with the Valid-Answer-Invalid-Reasoning (VAIR) dataset: math problems and solutions with trivial reasoning flaws but valid answers, designed to isolate reasoning evaluation from the confound of reasoning production. Unlike humans, who we find are only 6% worse at grading than solving such problems, we find a substantial production-evaluation gap in LRMs: frontier models score as low as 52% when evaluating VAIR solutions, despite near-perfect solution production. Why this enigma? Through chain-of-thought (CoT) analysis, we find evidence of an answer confirmation bias: LRMs often produce then check for the correct answer instead of carefully verifying each step, fabricating rationalizations even when noticing anomalous reasoning. Linear probes corroborate this, showing that while LRM activations encode some representation of valid reasoning, they fail to robustly represent VAIR solutions as invalid. Causal patching of the final answer's representations causes LRM verdicts and activations to flip at substantial rates, demonstrating that answer validity is a causal factor in models' confirmation biases. These findings indicate an outstanding limitation in dominant approaches to reasoning training, which incentivize LRMs to produce and confirm reasoning towards correct answers, but not to robustly evaluate the underlying reasons

cs.AI · cs.CL · cs.LG

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arXiv ↗PDF ↗