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新论文 · v1arXiv:2611.07487

When Does Blockwise Additivity Break? Diagnosing and Repairing the Additive Assumption in Distillation-Based Architecture Search

Yuefeng Xu, Jun Yu

原文摘要

Blockwise distillation methods for architecture search, such as DNA, DONNA, LANA and Puzzle, score each candidate replacement block in isolation and assemble a child network by minimizing the sum of per-block scores. We test this additive assumption on DeiT-S and DeiT-B with seven replacement blocks per layer, seven block-latency budgets from 70% to 40%, three fine-tuning seeds and paired bootstrap confidence intervals. (i) At mild compression the additive integer linear program (ILP) is close to a pairwise integer quadratic program (IQP) that models block interactions, but the post-fine-tuning gap widens sharply between the 60% and 55% budgets: at 55%-40% the ILP trails the IQP by 3.3-16.6 top-1 points on ImageNet-100. (ii) We split the KL additivity error into a metric component, caused by the non-linearity of the KL in summed logit changes and computable from cached single-block logits, and a functional component, the score-level effect of non-additive logit changes. The metric component accounts for most of the error on DeiT-S (59-65%), the functional component for most of it on DeiT-B (73-76%). (iii) A hybrid search ranks moves with the logit-sum predictor and measures only the best ones. It needs no dedicated pair measurements, costs 13-27% of the IQP's pair stage, and on ImageNet-100 significantly outperforms the IQP in 7 of 14 settings, with no significant difference in 6 and a significant loss in 1. Further reducing the pre-fine-tuning KL, or scoring after a short fine-tune, gives no consistent gain. On full ImageNet-1k with 1000-class fine-tuning, the sharp widening between 60% and 55% (5.8-17.5 points at 55%-40%), the model-dependent decomposition and the hybrid's advantage (significantly better than the IQP in 13 of 14 settings, no the other) are reproduced.

cs.NI · cs.NE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.08825

Autonomous Driving Research Requires a Community-Driven Data Paradigm

Jinsu Yoo, Zanming Huang, Katie Z Luo, Zheda Mai, Qiyuan Wu, Bharath Hariharan, Mark Campbell, Wei-Lun Chao

原文摘要

Autonomous driving has made remarkable progress, with recent AI advances enabling commercial deployments that are reshaping urban mobility. Yet the field remains far from its universal social promise: autonomous systems that can operate robustly anywhere, anytime, for anyone. We posit that this gap is not merely a modeling problem, but a problem of the prevailing data paradigm. Current research relies heavily on a few benchmark datasets with limited spatial and scenario coverage, even though the community has collectively produced over 600 autonomous driving datasets across nearly 50 countries. However, this abundance has not translated into broad research impact: most datasets remain significantly underused due to fragmentation, limited visibility, incompatible protocols, and benchmark incentives that concentrate attention on a few dominant datasets. We therefore argue that autonomous driving research requires a collaborative, community-driven data paradigm. Such a paradigm would improve the discovery, reuse, integration, and evaluation of diverse datasets; make underexplored data easier and more rewarding to study; and lower the barrier for new contributors. We outline its key principles, illustrate an early realization, and call for collaboration across academia and industry to transform fragmented datasets into shared community infrastructure for anytime-anywhere autonomy.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07486

MEvince: Knowledge-Constrained LLM Reasoning for Technique Identification from Malware Reports

Hongyu She, Yiling He, Mengda Xie, Yiping Liu, Xingzhi Qian, Zhan Qin, Lorenzo Cavallaro

原文摘要

Mapping behavioral intelligence in malware analysis reports to ATT&CK techniques supports threat analysis and defense, but reliable identification remains challenging. Supporting evidence is often scattered across long, heterogeneous reports, while closely related techniques share behavioral cues and differ only in subtle but decisive details. While large language models (LLMs) can use external knowledge to interpret reports, they may assign techniques based on plausible associations rather than sufficient evidence. We present MEvince, a knowledge-constrained LLM reasoning framework for report-level malware technique identification. Its core is a shared behavioral representation connecting report observations to technique definitions. This representation is built through agentic mining of technique documentation and supports two online stages: 1) primitive-guided extraction uses behavioral primitives to structure LLM-extracted observations, link them to source passages, and group dispersed evidence; 2) evidence-aligned retrieval matches this evidence to technique profiles through the same concepts, retaining likely candidates and confusable alternatives for LLM comparison of distinguishing behaviors. Controls at both stages validate evidence references and check technique-specific requirements. The representation also supports adaptation without parameter updates, where offline agentic audits diagnose concept weaknesses and refine extraction or retrieval policies. We evaluate MEvince against 11 baselines spanning four method families using a dataset of 610 public malware reports referenced by MITRE ATT&CK. MEvince improves exact-ID F1 over the strongest baseline by 17.4%, and its concept-guided retrieval increases gold-label candidate coverage from 31.6% to 47.9%. With a small adaptation set, offline refinement yields further gains and outperforms baselines that update model parameters.

cs.CR · cs.CL · cs.IR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2601.13308

Scaling laws for amplitude surrogates

Henning Bahl, Victor Bresó-Pla, Anja Butter, Joaquín Iturriza Ramirez

原文摘要

Scaling laws describing the dependence of neural network performance on the amount of training data, the spent compute, and the network size have emerged across a huge variety of machine learning task and datasets. In this work, we systematically investigate these scaling laws in the context of amplitude surrogates for particle physics. We show that the scaling coefficients are connected to the number of external particles of the process. Our results demonstrate that scaling laws are a useful tool to achieve desired precision targets.

hep-ph · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07483

Signed but Not Safe: Requirement Integrity in Multi-Agent LLM Handoffs

Xiaobo Chen, Genliang Zhu, Ziyuan Wang, Chu Wang

原文摘要

Enterprise AI agents increasingly split a user's request across subagents and merge their results, and each handoff can summarize away, alter or ignore a requirement. We report a preregistered study with synthetic, exactly scorable tasks comparing free-text, structured JSON and signed requirement-manifest handoffs: the orchestrator extracts each requirement as a verbatim quote plus a checkable predicate, code signs the manifest with HMAC-SHA256, and deterministic code verifies the merger's decision. With Qwen3-8B (1,440 trajectories, five threats), signed manifests cut unsafe commits under attack from 69.4% to 9.0% (60.4 pp, 95% CI [51.4, 69.4]) with no net loss of conformant commits relative to structured handoffs. Signing is what defeats in-transit tampering: an unsigned manifest committed violations in 79% of mutated runs, the signed one in none. On identical trajectories the deterministic verifier caught 78.7% of violating decisions versus 59.0% for an LLM checker, with 7% versus 50% false alarms. An oracle manifest removed all remaining unsafe commits: the residual risk comes from extraction errors made before signing. A preregistered pre-signing injection attack confirmed this boundary: one adversarial line raised signed-manifest violations from 14.6% to 37.5% (Qwen3-8B), 11.1% to 63.9% (gpt-oss-20b) and 8.3% to 75.0% (GPT-5.6-luna), and the signature then authenticated the corrupted manifest. Signing protects requirements in transit, not at extraction; the extraction step is the trust boundary.

cs.CR · cs.AI · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07482

SAVeR$^2$: Reasoning-based Safety Alignment for Large Reasoning Models via Verifiable Rewards

Yichen Sun, Qirui Liu, Yan Wang, Jianan Lin, Linbo Jiang, Shiyu Wang, Zhibo Wang, Zhixuan Chu

原文摘要

Large Reasoning Models (LRMs) have achieved impressive performance via explicit Chain-of-Thought (CoT) reasoning, yet this process introduces critical safety risks. We formalize LRM safety objectives as requiring non-malicious reasoning and answers, safety consistency, and explicit intent identification. We argue that existing alignment methods suffer from poor adversarial generalization, a significant ``safety tax'' on reasoning, and unfaithful coarse-grained rewards, primarily due to their neglect of the LRM's intrinsic reasoning capabilities for safety. To address these, we propose \textbf{SAVeR$^2$}, a novel \textbf{S}afety \textbf{A}lignment method via \textbf{R}ewards with \textbf{R}easoning capability. SAVeR$^2$ decomposes the safety objective into four fine-grained verifiable reward signals and utilizes a difficulty-aware data selection strategy to stabilize training. Crucially, we introduce a reasoning-preserving gradient projection mechanism that analytically resolves conflicts between safety and reasoning gradients, effectively eliminating the safety tax. Extensive experiments on models ranging from 1.5B to 671B demonstrate that SAVeR$^2$ achieves superior safety performance and significantly lower over-refusal rates across multiple benchmarks without compromising general reasoning capabilities.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.18938

Beyond Equation Search: Conjecture-Guided Discovery with SCOPE and ClosureBench

Yuxiao Hu, Qian Li, Ding Wang, Siheng Chen, Linfeng Zhang, Jinyue Yan, Yuntian Chen

原文摘要

Most symbolic regression methods optimize candidate equations, but improving an equation's fit does not establish whether its underlying scientific conjecture should be retained, revised, or rejected. A conjecture may admit multiple mathematical realizations, and similarly accurate equations may imply different explanations. This motivates a shift from refining individual expressions to developing and testing the conjectures that guide them. We introduce SCOPE, a framework that sustains multiple conjecture-driven research trajectories. Multiple scientist agents independently pursue conjectures through equation modeling, question formulation, and active experimentation, then exchange evidence to revise their conjectures and research directions. Their findings are consolidated into a scientific report linking models, conclusions, and evidence. Existing equation-discovery benchmarks primarily assess recovered expressions, leaving the evidence and scientific conclusions established through the investigation underexamined. We therefore introduce ClosureBench, an interactive benchmark of 60 closure-discovery tasks across five scientific domains. ClosureBench focuses on closure-discovery tasks to give equation discovery a concrete scientific purpose. Each task embeds an unknown algebraic relation in a known PDE system, where agents control experimental conditions in OpenFOAM to acquire new data for equation discovery. Beyond equation accuracy, ClosureBench proposes three metrics to evaluate the discovery process: evidence faithfulness, mechanism insight, and experimental effectiveness. Compared with diverse baselines, SCOPE achieves higher equation accuracy on both ClosureBench and a real-world scientific discovery case, while producing more accurate, reliable, and effective scientific discoveries.

cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.19297

CLaRE-ty Amid Chaos: Quantifying Representational Entanglement to Predict Ripple Effects in LLM Editing

Manit Baser, Alperen Yildiz, Dinil Mon Divakaran, Mohan Gurusamy

原文摘要

The static knowledge representations of large language models (LLMs) inevitably become outdated or incorrect over time. While model-editing techniques offer a promising solution by modifying a model's factual associations, they often produce unpredictable ripple effects, which are unintended behavioral changes that propagate even to the hidden space. In this work, we introduce CLaRE, a lightweight representation-level technique to identify where these ripple effects may occur. Unlike prior gradient-based methods, CLaRE quantifies entanglement between facts using forward activations from a single intermediate layer, avoiding costly backward passes. To enable systematic study, we prepare and analyse a corpus of 11,427 facts drawn from three existing datasets. Using CLaRE, we compute large-scale entanglement graphs of this corpus for multiple models, capturing how local edits propagate through representational space. These graphs enable stronger preservation sets for model editing, audit trails, efficient red-teaming, and scalable post-edit evaluation. In comparison to baselines, CLaRE achieves an average of 62.2% improvement in Spearman correlation with ripple effects while being 2.7x faster, and using 2.85x less peak GPU memory. Besides, CLaRE requires only a fraction of the storage needed by the baselines to compute and preserve fact representations. Our entanglement graphs and corpus are available at https://github.com/manitbaser/CLaRE.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.01215

Vision-Language Models Are Joint Denoisers in Robotic Action Generation

Xicheng Gong, Wenxuan Song, Chengze Liu, Qiwei Li, Qize Yu, Jiaolong Yang, Haoang Li, Yadong Mu

原文摘要

Diffusion-based vision-language-action (VLA) models cascade pretrained vision-language models (VLMs) with diffusion action experts, but typically restrict the VLM to providing static conditioning. This separation limits how much pretrained perception and reasoning can guide action generation, especially in out-of-distribution settings. We introduce JADE, a Joint Action DEnoiser that enables the VLM and action expert to jointly refine actions. Our key insight is that structured action latents allow the VLM to read the current noisy action together with visual observations and language instructions. At every denoising step, the VLM contextualizes the noisy action and guides the action expert through gated cross-attention, so that its guidance evolves with the action being generated. We further supervise the VLM to recover clean action latents from noisy ones, which gives it a direct action-learning signal and couples perception with control. Additionally, joint denoising enables JADE to generate actions with fewer sampling steps, helping offset the additional cost of VLM participation. An adaptive step-size schedule further balances task performance and inference latency. On RoboDojo, JADE achieves an average success rate of 12.67%, the highest among the evaluated $π_{0.5}$-based methods, and is competitive with recent VLA and WAM foundation models pretrained at scale. It also improves performance on RoboTwin 2.0 and raises the average success rate across four real-world bimanual tasks by 20.8 percentage points compared with $π_{0.5}$.

cs.RO · cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07481

Compact Motion Embeddings for Feed-forward Unified Human Animation

Seonwook Park, Hyunsoo Cha, Amrita Mazumdar, Shengze Wang, Tianye Li, Hanbyul Joo, Koki Nagano, Shalini De Mello

原文摘要

Animating a reference image with a driver's motion requires a motion representation that captures face, hand, and body articulation but excludes the driver's appearance. Most body animation methods take motion from external pose estimators and render it with many denoising steps; compact learned motion representations mostly cover the face. We present FUHA, a real-time-capable feed-forward transformer that learns a compact motion embedding of the whole human and uses it for face, upper-body, and full-body animation. A Q-Former compresses the driver's features from a frozen visual encoder into this embedding. To disentangle motion from appearance, a novel contrastive loss pulls it toward the motion embedding of the driver's rendered keypoint image and away from that of the same person in another pose. At inference time, FUHA needs no external pose estimators, keypoints, or parametric body models. As a retrieval key, the motion embedding matches body pose better than the learned global embeddings of ten vision encoders. Its same-person retrieval rate is 0.1%, against 9.5% for its own DINOv3 backbone. On our upper-body benchmark, FUHA outperforms all baselines on every self-reenactment metric and on cross-reenactment identity similarity. It is competitive on face and full-body and runs at up to 38.9 FPS at 512x512 on a single RTX 4090.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.06149

Grounding in the Dark: Text-Only Training for Zero-Shot Video Temporal Localization

Divyanshu Mishra, Sabine Sternig, Rakshith Shetty, Erhan Gundogdu

原文摘要

Video-LLMs are typically trained on paired video-text data for temporal tasks, yet collecting precise temporal annotations and training on large-scale video datasets are both costly. We investigate whether a text-only LLM, with no vision-language pretraining, can be trained on textual video descriptions alone to perform temporal grounding and receive video only at inference time. This setting removes paired video data from training altogether and is a step toward training multimodal models without paired annotations. We generate a 134K-example corpus of textual sequences with up to 100 time steps, adapt Qwen3-8B with LoRA on CLIP or LLM2CLIP text embeddings, and evaluate on Charades-STA and QV-Highlights without training on videos from either benchmark. LLM2CLIP, whose text encoder supports longer inputs, represents detailed descriptions better than CLIP (61.54 versus 53.86 mtIoU on Temporal-TextVid) and transfers them better to video. Still, replacing text with frame embeddings at inference yields only 4.99 mtIoU on Charades-STA and 11.12 on QV-Highlights, revealing a large modality gap. To bridge it, we propose Nearest Entity Replacement (NER), a training-free mapping that represents each video frame by the normalized mean of its nearest text-entity embeddings, so that the model receives the kind of input it was trained on. NER reaches 14.45 mtIoU on Charades-STA and 20.72 on QV-Highlights, outperforming the strongest of four families of modality-gap baselines by 5.00 and 1.71 points. NER thus enables partial transfer from text to video, while more reliable grounding will likely require encoders with better-aligned text and visual representations.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07478

Localization Does Not Imply Steerability: A Mechanistic Study of Sign Errors in LLM Mathematics

Shradha Agarwal, Tariq J., Deepak Rajbhar

原文摘要

When large language models do multi-step mathematics, a characteristic failure recurs: the model writes one wrong sign token mid-computation -- a cofactor sign, the minus of a recursive integration-by-parts step -- then executes the rest of the arithmetic correctly, and the answer comes out wrong. We ask where inside the network that sign is written. Across six open models spanning 14B to 675B parameters, a per-layer readout shows the wrong sign is committed late -- by MLP layers in the last quarter of the network (70-100% per model and task), a pattern present, where tested (Qwen), in the base model before instruction tuning. The mechanism varies: some models carry fixed "minus-stamp" layers that push a minus regardless of the right answer, others carry amplifiers of whichever sign was already chosen, and Qwen adds a downstream rescue layer that opposes the stamp. The circuit is causal: subtracting a single sign direction from the residual stream flips 13 of 14 held-out minus-written integration-by-parts errors on Llama-3.3-70B, with flips replicating on Qwen, Gemma, and Mistral-Small (24-70% per cell), and with representation-level specificity -- the same dose breaks correctly-minus answers while sparing correct-plus answers where the direction is surgical (0/72 on Llama), and matched random-direction and control-layer interventions are null. Core claims were preregistered with pass criteria frozen in git before held-out data was seen: 23 of 29 checks passed, all misses reported. But causality is not uniform: Phi-4 implements an equally well-localized circuit at the same late depth, yet the identical intervention flips almost nothing -- the sign is written redundantly across ~15 layers with margins several times larger (median 17.8 logits). Localization, we conclude, does not imply steerability: two models can share a circuit location yet differ enormously in how overturnable it is.

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07477

KOSMOS: Self-scheduling Persistent Kernels for Communication-Compute Overlap

Alexander Magro, Aristotle Martin, Ye Wang, Wen Chen

原文摘要

Tensor and sequence parallelism (TP+SP) and expert parallelism (EP) put collectives on the critical path of transformer layers. State-of-the-art fused kernels mitigate this with fixed roles or schedules, but this rigidity leaves large idle windows. Predicting a communication-computation split does not remove them either; a predetermined split cannot follow data readiness. We present KOSMOS, a library for self-scheduling persistent kernels whose workgroups use queues ordered by consumer demand to switch between communication and computation roles as data become ready, with no scheduler, per-call cost model, or tuned split. KOSMOS fuses every TP+SP collective of a transformer layer with its GEMM, and runs an entire expert layer, from router to fold, in one launch per direction. Evaluated on AMD Instinct MI350X and AMD Instinct MI355X GPUs, KOSMOS speeds up TP+SP end-to-end training in Megatron-LM by 1.10-1.28x in BF16 and 1.10-1.25x in MXFP8. KOSMOS accelerates end-to-end training of DeepSeek-V3 and Qwen3-235B proxies by 1.29-1.83x over DeepEP.

cs.DC · cs.AI · cs.LG · cs.PF

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07476

AMORT-V1: Checkpointed Post-Quantum Attestation for Industrial Controllers

Rishabh Das, Gerald Agendia, Jibriel Adjei, Ahmed Oun

原文摘要

Programmable logic controllers often remain deployed for decades, making post-quantum attestation a long-term requirement. Prior measurements of OpenPLC attestation show that post-quantum signing can consume 88--99% of round-trip latency and that durable state can further limit throughput. We introduce AMORT-V1, a checkpointed attestation protocol that moves public-key signing and durable commitment from individual reports to periodic checkpoints. An ML-KEM epoch establishes per-device evidence keys, devices authenticate reports with MACs, and the controller commits ordered evidence through hash-chained Merkle checkpoints signed with ML-DSA or SLH-DSA. Because shared MACs do not provide transferable proof of device origin, AMORT-V1 adds a device-held disclosure chain whose keys are released after checkpoint commitment. We validate the protocol with a 27-case attack matrix, 149 differential-test vectors, and a seven-node Raspberry Pi 5 testbed with five physical OpenPLC provers. Checkpointing reduces controller cost by 90 times relative to per-report ML-DSA without durable logging and by 878 times when durable logging is required. Transferable device-origin verification costs 2.95micro sec per report, while controller-side scaling to 10,000 identities requires 6.18 micro sec and 155 octets per report. AMORT-V1 also supports rollback-resistant restart through TPM-backed monotonic state without placing durable hardware operations on the per-report path.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2505.04897

CubeDAgger: Interactive Imitation Learning for Dynamic Systems with Efficient yet Low-risk Interaction

Taisuke Kobayashi

原文摘要

Interactive imitation learning makes an agent's control policy robust by stepwise supervisions from an expert. The recent algorithms mostly employ expert-agent switching systems to reduce the expert's burden by limitedly selecting the supervision timing. However, this approach is useful only for static tasks; in dynamic tasks, timing discrepancies cause abrupt changes in actions, losing the robot's dynamic stability. This paper therefore proposes a novel method, named CubeDAgger, which improves robustness with less dynamic stability violations even for dynamic tasks. The proposed method is designed on a baseline, EnsembleDAgger, with three improvements. The first adds a regularization to explicitly activate the threshold for deciding the supervision timing. The second transforms the expert-agent switching system to an optimal consensus system of multiple action candidates. Third, autoregressive colored noise is injected to the agent's actions for time-consistent exploration. These improvements are verified by simulations, showing that the trained policies are sufficiently robust while maintaining dynamic stability during interaction. Finally, real-robot scooping experiments with a human expert demonstrate that the proposed method can learn robust policies from scratch based on just 30 minutes of interaction. https://youtu.be/kBl3SCTnVEM

cs.RO · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07473

Echoes of Error: Residual-Directional Local Rollout Consistency for Timeseries Forecasting

Zheng Wang, Kaixuan Zhang, Xiaonan Lu, Wanfang Chen

原文摘要

In time-series forecasting, rollout errors are not merely one-step fitting discrepancies: once fed back into the evolving state, they can perturb future predictions. We formalize this effect through local rollout consistency, which compares the next prediction produced under teacher forcing with that produced under free running. In principle, this consistency can be enforced directly by tracking the residual-induced perturbation of the next state. In practice, however, the residual-to-state alignment operator is often difficult to design when the input state and output block have different dimensions, heterogeneous components, or architecture-dependent layouts. We therefore use a first-order local surrogate that penalizes the Jacobian-vector product of the predictor along the actual feedback perturbation, suppressing precisely the directions through which self-generated errors remain visible to future predictions. We further give the same loss a geometric interpretation near a predictive manifold: by controlling residual-induced directions that probe the normal bundle, the objective yields theoretical conditions under which free-running trajectories remain inside a stability tube around the manifold. Experiments on time series forecasting tasks support this view, showing that the proposed regularization improves predictive accuracy. More broadly, our results suggest a training principle for time-series forecasters and sequential predictors: a model should not only make small one-step errors, but also learn not to let its own errors echo into the future. Code is available at https://github.com/Wangzzzzzzzz/Echoes-Of-Error.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2610.19246

Refined Topology-Pass Scheduling: Damped Trust-Region Compensation for Dynamic k-NN Graphs in SE(3)-Equivariant Structure Prediction

Jiankai Huang, Hanwen Shen

原文摘要

Equivariant structure-prediction pipelines alternate coordinate updates with the construction of a k-nearest-neighbour (kNN) graph whose membership is a discrete, piecewise-constant function of the coordinates. Rebuilding that graph at every block boundary (PB) is the expensive part of an epoch, whereas reusing one frozen graph (F) is fast but leaves a topology gap. We study a two-pass design in which the cheap pass records a small amount of state (margins, kNN radii, measured per-boundary displacements) and a second pass compensates conservatively. We formulate the choice of the graph-construction basis as a damped fixed-point iteration whose unique fixed point is exactly PB, and derive (i) exact non-flip certificates with the two-sided constant $m_i/4$ and one-sided constant $m_i/2$, (ii) the minimum-variance (BLUE) replay gain $γ^*$ and the resulting feasibility crossover, (iii) an $\ell_\infty$ trust-region projection that preserves convergence, and (iv) an analytic near-tie spacing law $E[m] \approx r_k/(3k)$ that matches measurements to 1\%. Three compensation levels are implemented in C++ (L1: frozen set with live geometry; L2: margin-gated local repair; L3: damped, projected basis) and validated under a matched-conditions protocol (identical seed, MSA depth and trajectory family, only the topology mode differs). L2 reproduces the PB edge set exactly ($D = 0.0000$ at every boundary), L1 stays within 0.6% and L3 interpolates monotonically: L2 reproduces the PB edge set exactly when the trust region is removed, and L3 interpolates between the frozen and the PB topology. The recorded trajectory itself yields a measured $1.5\times$ Pass-1 and $1.76\times$ epoch speed-up with no measurable loss gap. An open-source implementation of the runtime and of all the experiments reported here is available at https://github.com/AlbertHuang96/ProteinPredictMachineLearning.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07470

Kinematic analysis of a double-closed-loop four-bar chain-based leg for a multimodal quadruped-pentarotor robot

Prashant Raturi, Santhakumar Mohan

原文摘要

This paper presents the kinematic modeling and singularity analysis of a three-degree-of-freedom, double four-bar closed-loop quadruped leg mechanism. This leg is essential for transitioning between the two operational modes; therefore, it is examined in both joint and task spaces, leading to the development of its Jacobian matrix, which is vital for designing the gaits required for terrestrial mode and mid-air aerial reconfiguration (for aerial mode). Various gaits are devised for the system's motion in the MATLAB simulation, and their practical implementation on the single-leg test rig is demonstrated.

cs.RO · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07469

Recursive Self-Improvement for Long-Horizon Role-Playing Dialogue

Y. L. Li, Y. H. Wu

原文摘要

Long-horizon role-playing dialogue requires consistent character identity, reliable memory, and context-sensitive interaction. We present a bounded recursive self-improvement (RSI) framework that revises original-character memory, stable persona instructions, and a policy for constructing turn-level prompts. Repair outcomes also inform revisions to the updater's diagnostic rules, edit routing, and candidate selection. We specify the updater as a versioned policy with explicit inputs, editable fields, acceptance gates, and stopping conditions. An internal evaluation covers 50 original characters and five models, with two 100-round conversations per character-model pair: 500 conversations per condition. Across the pooled evaluated replies, the bad-response rate decreases from 14.00% (6,973/49,824) to 4.94% (2,459/49,793), a 9.06-percentage-point reduction and a 64.71% relative reduction. In 600 blinded human comparisons, RSI receives 294 wins, 187 ties, and 119 losses, yielding a 71.19% win rate among decisive judgments. The results support improved aggregate dialogue quality for the complete framework on the internal test distribution.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.18857

PixelMoE: Scaling Pixel-Space Diffusion with LLM-Style Mixture-of-Experts

Ruixiao Dong, Jianmin Bao, Bozhi Luan, Zijie Meng, Xin Tao, Pengfei Wan, Kun Gai, Li Li 等 9 位作者

原文摘要

Pixel-space diffusion models provide an appealing alternative to latent-space diffusion models by directly modeling raw images without relying on a learned tokenizer. However, scaling pixel-space models has predominantly followed dense strategies, tightly coupling model capacity with active computation. In this work, we show that standard LLM-style Mixture-of-Experts (MoE) architectures can be effectively transferred to pixel-space diffusion. Building on this finding, we introduce PixelMoE, a pixel-space generative framework that sparsely scales model capacity with standard LLM-style MoE, without diffusion-specific routing mechanisms or auxiliary routing objectives. Specifically, PixelMoE combines standard Token-Choice (TC) routing with fine-grained routed experts for input-adaptive sparse computation, a shared expert for capturing common representations, and a bias-based load-balancing strategy. To complement sparse capacity scaling, PixelMoE further introduces Pixel-Space Hierarchical Flow Matching, which varies spatial resolution along the denoising trajectory from global structure formation to fine-grained detail refinement. At comparable active parameter counts, PixelMoE consistently outperforms its dense counterparts across model scales, demonstrating effective sparse capacity scaling in pixel space. On class-conditional ImageNet-1K, PixelMoE achieves FID scores of 1.47 at 256x256 and 1.53 at 512x512 without representation alignment or perceptual losses, comparing favorably with leading pixel-space and latent-space generative models. Code and pretrained models are available at https://github.com/KlingAIResearch/PixelMoE and https://huggingface.co/KlingTeam/PixelMoE, respectively.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.00365

Manifold-Constrained Initial Noise Optimization for Efficient Generative Model Alignment

Jinho Chang, Jong Chul Ye

原文摘要

Recent advances in distillation and flow-map models have enabled deterministic one- or few-step generation for high-quality data, facilitating a new branch of reward alignment approaches that directly optimize the initial noise from a Gaussian distribution. However, most existing initial-noise optimization methods rely on first-order gradient information, which is either inapplicable or suffers from instability and inefficiency in black-box reward scenarios. Here, we introduce ZeNOVA, a stable and efficient initial noise alignment method in a gradient-free manner. Specifically, we address existing algorithms' major challenge in black-box scenarios through annealed soft-value guidance, manifold-constrained hyperspherical Langevin dynamics, and Metropolis-Hastings jumping. Extensive experiments on image and video generative models show that ZeNOVA outperforms all evaluated zeroth-order baselines by optimizing the initial noise toward higher rewards substantially more stably while exploiting the geometry of the Gaussian prior, demonstrating its practical applicability to various black-box reward alignment.

cs.LG · cs.AI · cs.CV

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arXiv ↗PDF ↗
版本更新 · v7arXiv:2510.09885

Demasking Fine-Tuning for Knowledge Injection in Autoregressive and Diffusion LLMs

Xu Pan, Ely Hahami, Jingxuan Fan, Ziqian Xie, Haim Sompolinsky

原文摘要

Large language models (LLMs) are often used in environments where facts evolve, yet factual knowledge updates via fine-tuning on unstructured plain text often suffer from 1) reliance on paraphrasing augmentation, without which training does not generalize to downstream QA tasks, and 2) the reversal curse. Recent evidence of the sample efficiency of masked diffusion large language models (dLLMs) and the bidirectional information flow in their demasking training objective suggests that the demasking objective may facilitate knowledge learning from raw knowledge text. We test this hypothesis in controlled knowledge fine-tuning experiments on dLLMs and autoregressive LLMs (arLLMs) and find that while arLLMs rely on paraphrase augmentation to generalize knowledge text into question-answering (QA) capability, dLLMs do not require paraphrases to achieve high QA accuracy. We find that giving arLLMs a demasking objective, where the LLM must reconstruct the original text from a masked copy given in context, improves the efficacy of knowledge injection, removing the need for paraphrases and mitigating the reversal curse, recovering most of the gap between arLLMs and dLLMs. We also provide preliminary evidence of the broader applicability of using the demasking objective in arLLM fine-tuning on a large-scale knowledge-intensive dataset.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.33237

Each Insufficient in Its Own Way: Calibrating Language Models Across Varying Evidence States

Jiayi Li, Dennis L. Shung, Bradly C. Stadie

原文摘要

How can a language model know whether it has enough evidence to answer? Language models tend to answer even when the evidence they have is insufficient. Deciding whether to answer or gather more evidence requires a calibrated estimate of how likely an answer is to be correct with the available evidence. However, most calibration work treats the input as fixed, without asking how this estimate should change with the evidence. We introduce evidence-state calibration, which requires a model's confidence before answering to match its probability of answering correctly and to change accordingly whenever the evidence changes this probability. We propose SuffiCE (Sufficiency Confidence Estimation), which fine-tunes the confidence already present in a model's next-token distribution to match how often the model answers correctly from each evidence state. Across clinical diagnosis and multi-hop question answering with four models from two families, SuffiCE reduces the Brier score by up to 60% and increases AUROC by up to 0.16. Its confidence matches the empirical success rate and responds not only to the amount of evidence but also to which evidence is present, showing that models can learn to judge whether their current evidence is enough to answer.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07464

FINCOSMOS: An Open-World Evaluation Paradigm for Agents in Finance

Yupeng Cao, Xueqing Peng, Lingfei Qian, Haohang Li, Yangyang Yu, Anke Xu, Wenbo Cao, Yan Wang 等 17 位作者

原文摘要

Language agents increasingly work for users on the open Web, and finance is where trusting them is hardest. The data must come from the Web, the strategy lives in the user's head, and correctness depends on time, so an agent can fail silently: a strategy the user never asked for, unverified data, or a backtest that uses the future yields a report as convincing as a correct one. Existing benchmarks close the world somewhere, fixing the task, the data, or the timeline, and thereby remove these failures instead of measuring them. We introduce FINCOSMOS, an open-world evaluation paradigm for agents doing financial work. A simulated user holds a hidden strategy sampled from a compositional grammar and reveals it only when asked. The agent receives no data, so it must acquire data from the Web, build its own backtest under a data cutoff, and deliver verifiable results. Grading needs no reference answer. Programs verify point-in-time correctness and recompute every reported metric, the delivered strategy is checked against the hidden specification, and a run is accepted only when the user is satisfied and none of 15 veto checks fails. Across four coding agents and a model without tools in 150 sessions, no session is accepted: every session that the simulated user signed off fails a program check, and agents raise only 54--64\% of the details they were not told. Human experts attribute 88.33\% of failures to the agents. With FINCOSMOS feedback, an agent with persistent memory learned all ten tasks.

cs.AI · cs.CL · q-fin.CP

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.06559

From Fixed Defaults to Calibrated Activation Policies for Long-Term Time-Series Forecasting

Shijie Xu, Boyan Tang, Kai He, Jianghua Wu

原文摘要

Long-term time-series forecasting supports decisions in energy, transportation, and other applications where reliable predictions are needed beyond the observed data. Although forecasting research has advanced model architectures, fixed activation configurations provide limited guidance on how activation choices should vary across datasets and prediction horizons. This study aims to establish when task-specific activation choices improve forecasting accuracy and how previous experiments can guide the selection of suitable configurations for new tasks. We develop a calibrated selection policy that learns a reliable default from prior experiments, adapts the activation function and its location to each task, and retains the default when evidence for a change is insufficient. In an evaluation supported by 1,225 Informer training runs across 17 dataset variants and 51 forecasting tasks, the policy reduces mean squared error by 7.86% on average relative to the conventional fixed-activation baseline and improves 72.55% of tasks. These findings establish activation choice and placement as practical opportunities for improving existing forecasting models and show how accumulated experimental evidence can guide configuration decisions without repeating an exhaustive search for every task.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07462

Information-Spectrum General Formulas for the Capacity Region of Classical-Quantum Multiple Access Channels

Takuya Kubo, Hiroshi Nagaoka

原文摘要

For classical-quantum multiple access channels (cq-MACs) with two senders, we give several general expressions for the capacity region, valid for arbitrary sequences of channels. In the case of asymptotically vanishing error ($\varepsilon=0$), the capacity region is expressed in terms of spectral inf-mutual information rates. For every $0\le\varepsilon<1$, the $\varepsilon$-capacity region is expressed in terms of a quantity defined through a linear combination of three alternative hypotheses. In particular, the outer bound of Kubo and Nagaoka (2015) is tight. Our results include the quantum counterpart of the general formula of Han (1998) for the classical MAC. The most important ingredient is the one-shot inner bound of Sen (2021), which we apply to each member of an arbitrary channel sequence.

cs.IT · quant-ph

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2611.00177

AuroraWorld: Structured 4D World Modeling for Exo-to-Ego Video Generation in Robot Manipulation

Hao Xue, Peng-Fei Zhang, Ying Cheng, Lanyun Zhu, Lei Zhu, Heng Tao Shen

原文摘要

Egocentric observations provide a robot-centric view of manipulation processes, capturing contact interactions and object-state changes that are important for visuomotor policy learning but difficult to observe from external cameras. However, collecting such data at scale remains expensive due to additional hardware requirements and platform-specific deployment costs. Synthesizing egocentric observations from large-scale exocentric manipulation videos provides a scalable alternative. However, existing methods struggle to synthesize physically consistent and policy-relevant egocentric videos under substantial viewpoint transformations, severe occlusions, complex object interactions, and inherently sparse observations from available exocentric viewpoints. Code will be made publicly available at https://github.com/xueha0/AuroraWorld

cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07461

The Last Mile to the Real World: Agent-Native Primitives as Reusable Building Blocks for Solving Real-World Tasks

Haibo Jin, Peng Kuang, Xiaopeng Yuan, Lucheng Fu, Jerry Wang, Xucheng Yu, Yaoning Yu, Dehao Wu 等 14 位作者

原文摘要

How can an agent handle a real-world task end to end? Frontier models with their own harnesses still fall short on real-world benchmarks: on Terminal-Bench 4.0, GPT-5.6 Sol with Codex resolves 37.3% of the tasks and Claude Opus 5 with Claude Code 51.8%. These tasks fail at the step from reaching a resource to applying it, which we call the last mile to the real world. Existing approaches put the resource within reach but leave the competence to apply it with the caller, so each harness rebuilds that competence. We introduce agent-native primitives, reusable units that equip a resource with a resident LLM that applies it in context and communicates with other primitives, and HEELO (Harness Engineering around Evolving Agent-Native Primitives for the Last Mile to Real-WOrld Tasks), a harness engineering framework that builds each task's harness from a library of such primitives. HEELO composes the primitives a task needs into a task-specific graph, verifies the result against evidence from the environment, revises the primitives a failure implicates, and admits new or improved primitives after validation. We instantiate HEELO with eight primitive families, each built for one kind of task, and evaluate them on more than twenty benchmarks. Dev-Primitives raise Terminal-Bench 4.0 resolution from 37.3% under Codex to 55.5% on the same backbone, Harness Primitives composed per task raise Terminal-Bench 2.0 Pass@1 from 60.0% for the seed harness to 72.2%, Tool Primitives raise ToolBench pass rate from 55.8% to 75.1%, and Navigation Primitives raise GAIA-Text-103 accuracy from 45.6% to 58.3%. On the 46 real-world tasks of our heelo.dev platform, Claude Opus 5.5 with Claude Code and GPT-6.1 Sol with Codex complete 9 each on their own and 39 and 35 when connected to HEELO through our MCP server, with 1.9$\times$ and 2.3$\times$ less time and 1.55$\times$ lower cost per completed task.

cs.CL · cs.AI · cs.LG · cs.MA · cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07458

Physics-informed latent conditional flow matching for generative modeling of granular flows on inclined planes

Ziqi Han, Kuang Shi, Xuyang Li, Teng Man, Yimin Lu

原文摘要

The internal kinematic and mechanical states of granular flows are difficult to observe or measure, limiting the interpretation of internal particle interactions and their role in flow evolution. In this study, we propose a physics-informed latent conditional flow matching (PI-LCFM) framework for reconstructing complete multivariable flow sequences from partial boundary observations. A variational autoencoder and temporal flow-matching model learn a reusable latent prior over velocity, granular temperature, stress, and solid volume fraction. During inference, boundary-velocity or basal-stress observations and mass- and momentum-conservation constraints guide the latent trajectory through the decoded physical fields, without retraining either model. The results demonstrate that the model accurately reconstructs full spatiotemporal fields from observations containing only 0.3\% of the complete field information, extrapolates beyond the training ranges of inclination and particle number, and recovers key trends in runout scaling, energy evolution, and stress profiles. Compared with a frame-by-frame conditional flow-matching baseline, PI-LCFM reduces the mean velocity-component root-mean-square error by 45.2\%, while observation-guided reconstruction of 500-frame sequences achieves an approximately 44-fold speedup. Physics guidance further improves conservation consistency, supporting the broader potential of this framework for inferring hidden states in particulate systems, with potential applications envisioned in geophysical hazard assessment and industrial process monitoring.

cs.CE

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2603.20957

Alignment Whack-a-Mole : Finetuning Activates Verbatim Recall of Copyrighted Books in Large Language Models

Xinyue Liu, Niloofar Mireshghallah, Jane C. Ginsburg, Tuhin Chakrabarty

原文摘要

Frontier LLM companies have repeatedly assured courts and regulators that their models do not store copies of training data. They further rely on safety alignment strategies via RLHF, system prompts, and output filters to block verbatim regurgitation of copyrighted works, and have cited the efficacy of these measures in their legal defenses against copyright infringement claims. We show that finetuning bypasses these protections: by training models to expand plot summaries into full text, a task naturally suited for commercial writing assistants, we cause GPT-4o, Gemini-2.5-Pro, and DeepSeek-V3.1 to reproduce up to 85-90\% of held-out copyrighted books, with single verbatim spans exceeding 460 words, using only semantic descriptions as prompts and no actual book text. This extraction generalizes across authors: finetuning exclusively on Haruki Murakami's novels unlocks verbatim recall of copyrighted books from over 30 unrelated authors. The effect is not specific to any training author or corpus: random author pairs and public-domain finetuning data produce comparable extraction, while finetuning on synthetic text yields far little extraction, indicating that finetuning on individual authors' works reactivates latent memorization from pretraining. Three models from different providers memorize the same books in the same regions ($r \ge 0.90$), pointing to an industry-wide vulnerability. Our findings offer compelling evidence that model weights store copies of copyrighted works and that the security failures that manifest after finetuning on individual authors' works undermine a key premise of recent fair use rulings, where courts have conditioned favorable outcomes on the adequacy of measures preventing reproduction of protected expression.

cs.CL · cs.AI · cs.CY

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arXiv ↗PDF ↗