跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文，不再错过。

覆盖计算机全学科，以 AI 为快捷入口。关注方向、作者和关键词，把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+1196新增论文
+992其中 AI 相关
+66已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-10 11:15 · 全站最近入库：2026-11-10 11:14各信源进度 →

统计口径与数据新鲜度

统计 2026-11-09 11:59 — 2026-11-10 11:59（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-10 11:15最近发现新增版本：2026-11-10 11:14库内论文最新更新时间：2026-11-08 04:43

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

19,084 已收录论文16,280 AI 相关4,962 有修订112 HF 社区发现

浏览论文库

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 16,280 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-10 11:14（北京时间）
新论文 · v1arXiv:2611.02086

Reduced Cost Influence Functions for Predict-then-Optimize under Noisy Data

William Zhang, Saurabh Amin, Georgia Perakis

原文摘要

Machine learning models deployed in decision-making pipelines, from vehicle routing to dynamic pricing to clinical decision support, must remain reliable in the face of low-quality training data. We study a predict-then-optimize setting in which an upstream model predicts the cost vector of a downstream linear program, with a large pool of noisy training data supplemented by a small curated set of clean data. We introduce Reduced Cost Influence Functions (RCIFs), which combine influence functions from robust statistics with the geometry of linear optimization to estimate how each training point shifts downstream decisions. Building on this, we propose a reweighting algorithm that uses RCIFs to downweight harmful training points and reduce downstream regret. Experiments on random linear programs and shortest-path problems show that our approach reduces regret, and accompanying theory characterizes properties of RCIFs and identifies regimes favorable to our method relative to baselines.

stat.ML · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02084

Black Hole Love Numbers with Physics-Informed Neural Networks

Maria J. Rodriguez, Luis Fernando Temoche, Pavlos Protopapas

原文摘要

We develop and validate a physics-informed neural network (PINN) framework for computing the tidal Love numbers of black holes directly from their perturbation equations. We consider static perturbations of the Kerr black hole, whose exact hypergeometric solutions provide a controlled benchmark for both the numerical solver and the extraction of the physical response. The framework combines exact enforcement of the horizon boundary conditions with an $H^2$-Sobolev loss that incorporates derivatives of the differential-equation residual. The Love number is then obtained by extracting the asymptotic coefficients of the learned solution, without using the analytic connection formula. Across the tested scalar, vector, and tensor perturbations, the method recovers the finite dissipative response with relative errors below $10^{-5}$. Controlled comparisons show that the higher-derivative loss terms improve solution accuracy by orders of magnitude, while independent tests of the extraction procedure distinguish solver errors from errors in recovering the response coefficients. The conservative response, which vanishes analytically, is constrained by the numerical error floor. These results demonstrate that PINNs can accurately compute black-hole tidal response and establish a validated framework for future extensions to frequency-dependent and non-separable perturbation problems.

gr-qc · cs.LG · hep-th · math.NA

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02079

ParetoSaddler: Pareto-Guided Optimization of Agent Harnesses for Quality and Cost

Xingzhi Qian, Sungho Park, Rongyuan Tan, Jianbo Yu, Jue Zhang, Lorenzo Cavallaro, Menglin Xia, Yanzhi Chen 等 16 位作者

原文摘要

Harness optimization can substantially improve LLM agents without modifying model weights, but existing methods primarily optimize response quality. Extending them to joint quality--cost optimization introduces a challenge: applying the deployment preference during intermediate updates can prematurely reject candidates that are valuable for later improvements. We introduce ParetoSaddler, which addresses this mismatch by separating intermediate candidate promotion from final deployment selection. At each iteration, an optimizer updates a parent harness to produce a candidate, and then compares the two over repeated executions and uses Bayesian bootstrap Pareto support to promote candidates showing credible progress in either quality or cost. Promoted candidates receive broader development evaluation and remain available as parents for later optimization steps. We evaluate ParetoSaddler on ClawMark and GAIA2 using domain-disjoint splits. On ClawMark, ParetoSaddler improves quality from 70.86\% to 81.35\% while reducing cost from \$1.17 to \$1.01 per task; on GAIA2, it improves quality from 56.22\% to 62.89\% while reducing cost from \$0.78 to \$0.45 per task. The Pareto-selected harnesses also achieve higher quality at lower cost than quality-only harness optimization. Analysis shows that Pareto-aware optimization retains intermediate candidates that would be discarded by deployment preference but can support later improvements, while Bayesian bootstrap screening filters out candidates whose gains are weakly supported across runs.

cs.AI · cs.CL · cs.LG · cs.MA

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02076

Scene-Adaptive Readout and Decoding in Diffusion Driving VLAs

Seungjun Yu, Jiho Choi, Inha Kang, Hojun Choi, Seungwook Kim, Hyunjung Shim

原文摘要

Diffusion driving vision-language-action models repeatedly execute the backbone at every denoising stage, incurring substantial inference cost, while it remains unclear whether all scenes require the same decoding computation. To distinguish when a trajectory becomes readable from how much further refinement it requires, we analyze trajectory readout across network depth and decoding progress. We identify a depth-decoding dissociation in which late-layer representations already support useful trajectory readout after the first forward sweep, whereas early-layer representations benefit more from continued decoding. We further find that both the preferred readout layer and decoding depth vary across scenes. Based on these observations, we propose a scene-adaptive readout and decoding framework. A scene-conditioned readout MoE adaptively weights and combines trajectory predictions from layer-specific late-layer representations after a single backbone sweep, while an adaptive decoding policy applies additional native refinement only when required by the scene. We evaluate our framework on two diffusion driving VLAs across the Waymo E2E and nuScenes benchmarks. Our method improves validation RFS by up to 0.56 while also improving test performance, and achieves an end-to-end speedup of up to $6.0\times$ on Waymo E2E, while consistently reducing trajectory error on nuScenes. These results demonstrate that scene-level readout and decoding allocation improves planning quality while avoiding unnecessary decoding. Our code is available at https://github.com/sjyu001/SARD

cs.CV · cs.AI · cs.RO

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02075

Decoupled Predictive Decision Making: A Shared Conditional Predictor for Agents

Lang Mo, Xingxuan Zhang, Qi Wang, Mingchao Hao, Hao Yuan, Chunjing Gan, Wang Lu, Peng Cui

原文摘要

Agent decisions depend on empirical relationships that deployment observations reveal and task descriptions alone may leave unresolved. We introduce Decoupled Predictive Decision Making (DPDM), an architecture that formulates heterogeneous agent decisions as conditional prediction with a shared pretrained learner. Language models provide semantic representations of current and historical records, while a general-purpose conditional predictor predicts task-defined targets from the represented evidence. Pretraining supplies reusable inference capability; deployment evidence instantiates problem-specific predictions without updating either model's neural parameters. Tool judgments, candidate preferences, and reasoning assessments share an evidence-and-query interface, with predictions mapped to choices by a decision rule. This architecture establishes complementary sources of agent capability: semantic knowledge acquired through language pretraining and empirical inference acquired through structured-data pretraining. A single frozen LimiX-2 predictor serves all five task categories. With Qwen3.5-27B as the semantic encoder, DPDM raises mean accuracy across 50 benchmarks from 78.80% with few-shot prompting to 84.92% under their respective evaluated history budgets, and reaches 82.33% with identical histories and queries. On ToolPRMBench's BFCL and ToolSandbox tasks, absolute accuracy gains are 34.26% and 28.87%, respectively, at the methods' respective evaluated history budgets. Accuracy improves as history grows. With identical imbalanced histories, mean balanced accuracy improves by an absolute 14.51% across five reasoning-step assessment tasks and four encoders. DPDM makes learning from experience an independently improvable foundation-model capability, creating a shared route for advances in conditional prediction to improve decisions across agent tasks.

cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02074

SWE-Trail: History-Guided Trajectory Collection for Coding Agent Training

Ziming Li, Lu Yu, Jiatan Huang, Qing Cui, Jun Zhou, Chuxu Zhang

原文摘要

Coding agents solve repository issues through long sequences of exploration, editing, and validation. These trajectories are increasingly used as post-training data, so how they are collected shapes what the trained model learns. A common approach is to sample multiple trajectories independently for each issue and then select or curate them. However, as more trajectories are sampled independently, later trajectories become increasingly redundant, even though earlier trajectories could guide where subsequent collection effort should be spent. A natural way to use this history is to expose it to the coding agent as advice. But training on such trajectories can make the model rely on advice that is unavailable at test time, creating a train-test mismatch. Instead, we argue that history should guide where collection effort is spent, while the coding agent decides what to generate. We introduce SWE-Trail, a trajectory collector that uses earlier trajectories to guide subsequent collection while keeping that history outside the coding agent's context. During a new rollout, SWE-Trail uses history to identify a state where additional collection effort may be useful. There, the coding agent proposes candidate next actions from its ordinary context, and SWE-Trail briefly executes them from the same checkpoint to decide which route continues. We evaluate collection approaches by training student models on the trajectories they produce. For a 124B student initialized from Ling-3.0-flash-base, SWE-Trail improves mean resolution over independent sampling by 2.33 and 1.60 percentage points on SWE-bench Multilingual and SWE-bench Pro, respectively. Without history, the same pipeline produces more resolved trajectories than independent sampling, but improves by only 0.11 points on Multilingual. These results show the value of using history to guide collection without exposing it to the coding agent.

cs.AI · cs.SE

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02073

Back to the Future: Predicting Long-Horizon Dynamics with Retrospective Correction for Disturbance-Aware Retrieval

Jiwoong Choi, Haoyu Li, Yiwei Lyu

原文摘要

Retrieving objects from densely packed storage requires anticipating how contact disturbs surrounding objects when limited clearance prevents lifting the target over clutter. However, contact can propagate beyond the target's immediate neighbors, while accumulated prediction errors make learned dynamics unreliable over long planning horizons. We introduce DARE (Disturbance-Aware REtrieval), a geometry-aware relational dynamics framework that uses retrospective correction to improve long-horizon prediction for disturbance-aware retrieval. DARE encodes compact object descriptors and predicts action-conditioned disturbance propagation through object-level message passing. When a sparse observation becomes available, a learned corrector uses the prediction discrepancy to amend an earlier predicted state and reruns the intervening dynamics, propagating the correction through the predicted contact chain. These predictions guide a receding-horizon planner that balances retrieval progress against displacement and rotation of surrounding objects, permitting necessary contact while discouraging harmful disturbance. Experiments in planar and RoboCasa environments evaluate long-horizon prediction, generalization, and robustness for disturbance-aware retrieval in dense clutter. See our project website: https://the-back-to-the-future.github.io

cs.RO · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02067

State-Aware Virtual Patient Simulation for Training Psychiatrists in Challenging Clinical Scenarios

Harsh Mohta, C Chandrahas Reddy, Sunhith Reddy Bojja, Manik Inder Singh Sethi, Vinesh Gupta, Raghuram Bharadwaj Diddigi

原文摘要

Psychiatrists must be prepared to manage patients exhibiting challenging psychological states, including suicidal ideation, hallucinations, and other high-risk behaviors. However, opportunities to safely practice such interactions are limited. While recent conversational agents can simulate patients, existing approaches largely rely on large language models (LLMs) to determine patient responses, making it difficult to consistently maintain psychological state, adapt to the clinician's responses, and generate clinically plausible challenges that meaningfully test the clinician. We present a state-aware virtual patient simulation pipeline for training psychiatrists in challenging clinical scenarios. The proposed system maintains an evolving patient state throughout the interaction and orchestrates patient behavior rather than leaving response generation entirely to an LLM. At each interaction, a learning-based behavioral strategy selector determines the type and level of challenge appropriate to the current patient and clinician context. The selected strategy, patient state, clinician response, interaction history, and relevant clinical knowledge are then provided as grounded context to an LLM to generate a semantically relevant and psychologically consistent patient response. Following each session, the system analyzes the interaction and provides feedback to the clinician on their communication and management of the virtual patient.

cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02066

Large Geometry Models Are Priors For Fast Physics Pretraining

Maggie Chlon, Leon Chlon

原文摘要

Language and vision models became data-efficient by pretraining on plentiful unlabelled data; physical field models have such data, over ten million 3D shapes, but geometry pretraining designs its targets rather than deriving them from the physics. Euler-Lagrange geometry pretraining derives them: for attached flow, a shape enters the flow's Euler-Lagrange equation only through coefficients that distance and normal line (the normal up to sign) fix to first order. Their reconstruction error therefore bounds what a representation loses about any field, and fine-tuning on costly simulations need only assemble the global field. We realise it in AREALS, a masked surface autoencoder whose areal tokens carry position, normal and area, pretrained self-supervised and sign-free on 61,822 raw meshes. Because the operating condition enters only through the far field, sensors can supply it: AREALS completes a hidden field in one forward pass without condition labels. It matches the accuracy of the same architecture trained from scratch with half the simulations on DrivAerML and DrivAerNet++ and about a quarter on WindsorML. On DoubleDelta wings, 20 sensors give $3.9\times$ and $4.0\times$ lower field and force error than GABI, a Bayesian inversion method with a learned prior, and beyond the training angles pretraining halves the error.

cs.LG · stat.ML

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02065

Fragility Is Not Reducible to Hardness: Conditional Causal Difficulty for LLMs

Mohammad Reza Rezaei, Dingxian Wang, Nikhil Verma, Sajjad Pakdamansavoji, Andrew Rabinovich, Zhao Chen

原文摘要

Large language models show large and seemingly arbitrary performance drops when a task is rewritten in ways that leave its requirements untouched. We recast task difficulty as a causal quantity: the treatment effect of meaning-preserving perturbations on a model. A meaning-preserving rewrite can only be observed to hurt a task the model can already solve, so we place the estimand on the principal stratum where the degradation occurs: the conditional causal difficulty (CCD), the probability that a randomly drawn twin breaks a solvable task. Because harder tasks tend to be more phrasing-sensitive, complexity enters as an effect modifier rather than a confounder, and we residualize it out to obtain complexity-residualized difficulty (CRD); zero for a task exactly as fragile as its complexity predicts, positive for anomalously fragile ones. Current benchmarks do not supply the data CCD requires, so we introduce GlassGSM and GlassMATH, augmenting GSM8K and MATH with twins built from perturbation families, raising fragility on GlassGSM by $\approx 12\times$ over GSM-IC-style distractors on the same problems ($3.7\%\to44.9\%$) while keeping the answer key unchanged. Our predictor Frail learns the per-perturbation break probability and reaches $0.613{\pm}0.023$ of the suite reliability ceiling on GlassGSM and $0.557{\pm}0.023$ on GlassMATH, against $0.171$/$0.401$ for a surface-feature baseline and $0.444$/$0.463$ for supervised baselines such as VAP3, and, at a $5\%$ curation budget on GlassGSM, doubles random recall of fragile items, matching an oracle ranking. Additionally, cross-fitted residualization leaves CRD uncorrelated with the declared proxy in practice. Code is available at https://github.com/upwork/Conditional_Causal_Difficulty_for_LLMs

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02056

Do Embodied VLMs Actually Use Their Scene-Graph Memory?

Shubh Garg, Divyansh Pandey, Vedant Khandelwal, Niyati Rawal, Amit Sheth

原文摘要

Embodied agents that answer questions about their route need a memory of what they saw, and scene graphs are a natural form for it. A vision-language model (VLM) can receive such a graph as text or through a graph encoder, but accuracy alone cannot show whether the graph holds the episode's facts, reaches the model, or is actually relied on. In this diagnostic study, we introduce a graph-swap test for the last of these: each episode is given another episode's graph, and we count how many answers change. On an embodied question-answering benchmark with routes through two to seven rooms, we use it to compare ways of delivering scene-graph memory to a fine-tuned VLM. As text, the memory is used strongly, adding 30 points on route questions. As a graph, it is not, and we trace why: the encoder preserves the information, but standard pooled and late fusion discard it. Giving each node its own input token recovers part of it, a gain of 8.3 points on route questions that the swap test confirms comes from the graph. The test also exposes a shortcut that accuracy hides: when the same facts appear in the text, the model ignores its graph. The test thus offers a cheap check of whether a VLM uses its scene-graph memory, and our analysis points to how that memory should be delivered.

cs.RO · cs.AI · cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02055

Rare Event Constrained Reinforcement Learning with Large Deviations

Rahul Misra

原文摘要

We study finite-horizon reinforcement learning with a safety constraint on the probability of reaching a failure set. In contrast to the well-studied safe reinforcement learning literature, we consider problems where the probability of reaching the failure set is vanishingly small, but the consequences of reaching the failure set are catastrophic. Direct simulation and estimation of the optimal policy is naturally ineffective when the failure probability is extremely small. Moreover, on a real system the controller can only choose actions, and this is not enough to make failures frequent. In contrast to the rare event simulation literature, our goal is not to estimate the probability of the rare event occurrence, but to upper bound it such that the failure constraint is satisfied. Thus, instead of sampling failures, we use the large deviations principle. We define a damage variable that on exceeding a certain threshold indicates failure. We study the cumulant generating function (CGF) as its positive root provides an upper bound on the failure probability via Cramer's theorem. We extend this approach to Markov decision processes with hitting times through state-action CGFs, and give finite-horizon and state-dependent versions of the bound. The state-action CGFs do not depend on the policy and can be estimated from regular transitions, so the bound can be checked for any policy a priori. This gives a policy improvement algorithm in which every policy satisfies the failure constraint with high probability.

stat.ML · cs.LG · eess.SY · math.OC · math.PR · math.ST

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.21633

HERALD: High-Throughput Block Diffusion LLM Inference via CPU-GPU Cooperative KV Cache Retrieval

Omin Kwon, Doyeon Kim, Jongseok Park, Seung Yul Lee, Sehoon Kim, Ion Stoica, Jae W. Lee

原文摘要

Block diffusion LLMs (block dLLMs) improve decode throughput by generating tokens in parallel, and are emerging as a next-generation alternative to autoregressive LLMs. At long contexts, however, the KV cache remains a major throughput bottleneck in both memory bandwidth and capacity. For autoregressive LLMs, dynamic sparse attention is often combined with KV offloading to alleviate both bottlenecks, keeping the full KV cache in CPU DRAM and fetching only a critical subset to the GPU. However, KV subset selection and retrieval over bandwidth-limited PCIe incur substantial offloading overheads that often stall the GPU. We present HERALD, the first dynamic sparse attention method for block dLLMs that supports KV offloading. HERALD exploits a property of block dLLMs: a critical KV subset selected once per block is reused across all of its denoising steps, so the next block's selection can be hidden behind the current block's denoising. HERALD realizes this with a two-stage selection algorithm co-designed with its execution system. While the GPU denoises the current block, the CPU speculatively searches the full KV cache for the next block with a single query merged from the current block's B queries, cutting the selection compute by Bx; the GPU then refines the candidates into the final top-K subset with the next block's real queries. At K=1024, HERALD achieves near-lossless accuracy relative to Dense on LongBench and reasoning tasks across four block dLLMs, and improves decode throughput by up to 3.70x over Dense GPU-only inference, 2.59x over MAGE, a state-of-the-art block-dLLM sparse attention method, and 8.38x over MAGE-Offload, its direct KV-offloading extension.

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03735

HyVox: Rethinking Low-Power Voice Intelligence towards Neuromorphic-von Neumann Edge Computing

Snehasis Addy, Eduardo Calle Ortiz, Prashant Shenoy, Phuc Nguyen

原文摘要

We present HyVox, a hybrid neuromorphic--von Neumann system for low-energy open-set speaker identification under tight energy budgets. HyVox splits the workload at the speaker embedding. A neuromorphic processor embeds the speech with sparse, event-driven computation, and a von Neumann microcontroller holds the speaker gallery and makes every decision: it scores queries by cosine similarity, rejects unknown speakers, and enrolls new users by writing a prototype, with no weight update. On the neuromorphic processor, a hierarchical temporal abstraction (HTA) SNN backbone learns a synaptic time constant per leaky integrate-and-fire (LIF) neuron, allocating 832 neurons across fast, medium, and slow timescales. A 32-neuron spiking embedding extractor (SEE), pretrained on episodes that each enroll new speakers and reject outsiders, turns the backbone output into an embedding for speakers unseen in training; its 32 spike counts are the only interface between the two processors. To fit the chip's synapse and integer budgets, we compress the backbone 6.5x to 29.0k synapses, train it for integer bit-shift dynamics, and fine-tune it with chip-accurate spiking training (CAST). CAST trains through a differentiable replica of the chip's integer neuron update, derived from its cycle-accurate simulator, so that gradient descent optimizes the exact weights and thresholds the chip runs; it doubles the top-1 accuracy of the mapped backbone on unseen speakers. On 20 Common Voice households of unseen speakers, HyVox reaches 99.6\% top-1 and 5.5\% rejection EER with 2 members and 94.9\% and 13.2\% with 20, and the network deployed on the neuromorphic chip matches its trained model. Each inference on a 3-s window takes 0.96 s and 1.63 mJ of dynamic energy, 42\% lower latency than the fastest embedded ANN baseline and 13.5x less dynamic and 5.8x less total energy than the most energy-efficient one.

cs.NE · cs.AI · cs.LG · cs.SD

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02053

Learning to Look: A Foundation for Visual Understanding

Wenshuai Zhao, Yihao Wang, Benjamin Robson, Juho Kannala, Arno Solin

原文摘要

Vision-language models (VLMs) suffer from visual hallucination, such as saying an object that is not present in an image. We ask a more fundamental question: whether a VLM is even aware of where it is looking before understanding the semantic meaning in the image. We formulate this question as a LOOK task: identifying which parts of its visual input were available to the model. We construct the task in a self-supervised way without manually labeling the available region to the model. Our experiments show that existing VLMs fail to localize the visible region. We further show that fine-tuning VLMs on the proposed LOOK task improves a model's awareness of visual input.

cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02052

Same Function, Different Gradients: Evaluating Gradient Reliability under Reparameterization

Bum Jun Kim

原文摘要

Gradients are no longer used only to train models: Gradient magnitudes are now measurement instruments, used to rank attention heads for pruning, to decide per-example clipping in private training, to allocate adapter rank budgets, and to debug models. Across these workflows, larger gradient magnitudes are taken to mean greater importance. Even for a fixed input--output function, these gradients are computed in one of many possible parameter coordinate systems. For example, attention scores depend on the query and key projections only through the paired product $W_QW_K^\top$. Scaling a head's query projection by $c$ and the corresponding key projection by $1/c$ therefore leaves every output logit unchanged. This change in parameter coordinates rescales the corresponding query and key gradient norms by $1/c$ and $c$, respectively. To test whether gradient-based decisions remain unchanged under these coordinate changes, we introduce Gradient Reliability under Reparameterization (GRR). GRR rewrites a trained model in new parameter coordinates that compute exactly the same function, verifies that predictions are unchanged, and then tests whether the decisions derived from the gradients are unchanged too. Our audits span GLUE classifiers, a ViT on CIFAR-10, GPT-2-medium and GPT-2-large, a 1.1B-parameter Llama-style model on WikiText-2, and six trained LoRA adapters. Empirically, predictions are preserved throughout, but both the gradients and the gradient-based decisions change. Across 66 GLUE attention audits, the median top-3 overlap of raw head-gradient rankings is only $1/3$, and rankings and pruning masks change even under the mildest rescalings we test. In the largest of 14 global-pruning audits, the shifted ranking selects head masks that differ in four of 22 pruned heads and reach $50.92\%$ and $94.84\%$ validation accuracy on one ELECTRA SST-2 model.

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02051

UniGS-SLAM: Monocular Gaussian SLAM with Enhanced Geometry and Open-Vocabulary Semantics

Xingyuan Ding, Sicheng Yu, Kaiyong Zhao, Hao Wang

原文摘要

Monocular semantic SLAM aims to reconstruct scene geometry and build semantic maps from RGB observations. Existing Gaussian-based methods often attach semantic labels or features to evolving Gaussian primitives, making semantic fusion difficult to maintain and increasing reconstruction overhead. Hybrid Gaussian--voxel representations provide more stable semantic support, but often rely on RGB-D input or known poses and do not revise historical semantic evidence after geometry updates. We present UniGS-SLAM, an RGB-only framework that decouples open-vocabulary semantics from Gaussian reconstruction while preserving bidirectional interaction. Foundation priors enhance monocular depth and pose estimation, while semantic observations are fused into an independent sparse voxel map with observation provenance, enabling historical contributions to be revised as reconstruction evolves. The fused semantics further guide Gaussian filtering and local surface refinement. Experiments on Replica, ScanNet, and KITTI-360 show leading tracking, rendering, and geometric reconstruction performance among representative RGB-only methods, together with strong open-set semantic mapping on Replica and ScanNet. We also achieve a 2.37-fold reconstruction speedup over the fastest compared semantic SLAM baseline. Project page: https://3dagentworld.github.io/UniGS-SLAM/.

cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02049

Reinforcement Learning in High-Energy Physics: A Survey from Accelerator Control to Theory Exploration

Luca Anzalone

原文摘要

Reinforcement learning (RL) is a machine learning paradigm in which an agent learns a decision-making policy by interacting with an environment, guided by a scalar reward rather than explicit supervision. Supervised and unsupervised learning have become indispensable tools in high-energy physics (HEP), but they learn from fixed datasets, whereas many HEP tasks are closed-loop decisions: the data an algorithm observes depend on its choices, the feedback is sparse or delayed, and the objective is often measured on a real machine or in a stochastic simulation, not by a differentiable model. This survey reviews the application of RL to such tasks: accelerator control and feedback, data quality monitoring, jet grooming, clustering, and representation, charged particle tracking, beyond-the-Standard-Model (BSM) and string model building, detector design, trigger control, analysis, and formal quantum field theory computation. It provides a self-contained introduction to the underlying RL methods, from model-free and model-based to multi-agent, offline, inverse, and bandit approaches, reviews each domain against its traditional methods, and summarizes the head-to-head comparisons. The applications range from policies deployed on real accelerators and evaluated on recorded collision data to exploratory studies in formal theory. RL is most advantageous for repeated tasks with a fast simulator, decisions inside fast control loops, operation under faults and corrupted measurements, physics-based rewards in place of labels, and sequential discrete constructions with a reusable policy, while Bayesian optimization, genetic algorithms, and differentiable methods remain competitive elsewhere. We conclude with the open challenges that recur across domains, including reward design, sim-to-real transfer, safety, the lack of shared benchmarks, and the statistical validity of policy-driven decisions.

hep-ex · cs.LG · hep-ph

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2403.10232

Over-fitting Control in Deep Nonlinear Matrix Completion via Gradual Nonsmooth Regularization

Sajad Faramarzi, Farzan Haddadi, Sajjad Amini, Masoud Ahookhosh, Symeon Chatzinotas

原文摘要

Conventional matrix completion methods exploit the underlying low-rank structure of a target matrix to recover its missing entries, but may be inadequate for capturing complex nonlinear dependencies. Deep neural networks have therefore been increasingly adopted for nonlinear matrix completion (NLMC). However, their high representational capacity, particularly in deep fully connected neural networks (FCNNs), can lead to over-fitting when training is restricted to incomplete observations. In this paper, we propose a Deep Neural Network with Nonsmooth Regularization (DNN-NSR) framework to address this issue by incorporating two complementary structural priors: sparsity of intermediate representations and low-rankness of layer-wise weight matrices. Specifically, the $\ell_1$-norm of hidden representations promotes sparse latent features, while the nuclear norm of weight matrices encourages low-rank layer-wise transformations. To optimize the resulting nonsmooth and nonconvex objective, we develop an extrapolated proximal gradient algorithm that employs soft-thresholding and singular-value thresholding to handle the two regularization terms, respectively. We further introduce a gradual regularization strategy that progressively increases the influence of these structural penalties during training. Under suitable assumptions, we establish convergence properties of the proposed algorithm toward a critical point of the training objective. The effectiveness of DNN-NSR is evaluated through extensive experiments on synthetic matrix recovery, single-image inpainting, and recommender systems using the MovieLens 100K and MovieLens 1M datasets. Comparisons with representative linear and nonlinear matrix completion methods demonstrate the potential of the proposed framework to improve recovery performance by jointly exploiting sparse latent representations and low-rank network transformations.

cs.IT · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02047

Beliefs in feedback: Factored representations of interacting agents in transformers

David A. Ehrlich, Manuel Baltieri, Fernando E. Rosas, Alexander B. Boyd

原文摘要

Complex systems, from ecosystems to brains, are usually modeled as modular components coupled through feedback rather than as indivisible wholes. Can a transformer trained on the joint behavior of a group of interacting agents be understood in the same way? In this work, we develop a computational-mechanics framework to study agents interacting in feedback loops. We formally prove that any joint stochastic process can be presented as resulting from the behavior of agents interacting in feedback, and the resulting process is generated by a hidden Markov model whose Bayesian beliefs factorize into separate components per agent. Furthermore, our experiments show that transformers trained on such feedback sequences make use of this separability, disentangling the beliefs about the underlying factors into separate subspaces of their residual stream. This representation is found to scale linearly (instead of exponentially) with the number of agents. Moreover, interventions show that these representations are causally efficacious, allowing for targeted steering of individual agents' beliefs by selectively inserting counterfactuals. However, coarse-graining the behavior is found to disrupt the factorization, and can force a higher-dimensional representation. Together, the results establish both the dimensional advantage of a factorized representation and the ability of transformers to exploit it, laying the groundwork for investigating traces of compositional predictive architecture in pretrained models.

cs.LG · cs.AI · cs.MA · nlin.AO

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02045

METER: Matched-Event Calibration for Short-Event EEG Microstate Transition Analysis

Abdul Basit, Eklavya Tomar, Muhammad Shafique

原文摘要

Short cognitive events contain unequal and often sparse transition evidence, so raw EEG microstate distances can reflect finite sampling rather than neural reconfiguration. We introduce METER (Matched-Event Transition Estimation against a Reference), a participant-relative framework that separates marginal occupancy from conditional next-state choice and evaluates each event against duration-conditioned events from a fixed participant-specific reference bank. METER-Jump integrates uncertainty in sparse target and reference transition tables, returning a randomized event rank for testing and a continuous standardized coordinate for association analysis. Using a reproducible three-state representation selected by spatial criteria, we evaluated METER with participant-fitted simulations, estimator ablations, map perturbations, and preprocessing sensitivities. Event-level false-positive rates were 0.044-0.056 across four heterogeneous null mechanisms. By contrast, global cutoffs learned under the participant-fitted null and transferred unchanged to other regimes produced false-positive rates from 0.004 under rare transitions to 0.234 under map perturbation; duration-conditioned ranks remained near 0.05. Detection probability was approximately 0.69 with fixed maps but fell to 0.22 after bootstrap-derived map-assignment perturbation. In 4,175 natural-reading events from 18 participants, no linguistic association survived multiplicity correction in the primary 30-ms analysis, although a 20-ms sensitivity changed one within-stream max-$T$ decision. METER therefore calibrates finite-event departures conditional on a defined microstate sequence, while map assignment, filtering, temporal smoothing, and reference construction remain separate sources of measurement uncertainty.

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02041

ECGJev: Question-Conditioned Option Scoring for 12-Lead ECG Interpretation

Zhuoyuan Li, Xidong Wu, Hanyu Cui, Jiansong Gao, Rongrong Fu, Mingke Yan, Haotian Tang

原文摘要

Clinical interpretation of a 12-lead electrocardiogram (ECG) often requires answering targeted diagnostic questions with clear, quantifiable decisions. We introduce ECGJev, the first ECG framework built on a decision decoder, a pretrained language model trained to return a probability for every listed option. A question-conditioned bridge compresses frozen waveform and spectrogram features into 16 ECG tokens, and a two-stage procedure adapts the bridge and low-rank adapters on the decoder's native option readout using diagnostic labels alone, scoring all options for a question in one forward pass. Evaluated across four ECG benchmarks, ECGJev achieves the highest macro-F1 on CPSC2018 (80.46%), PTB-XL (71.68%) and Chapman (96.86%), and generalizes competitively to the small-sample PTB cohort. Concept probes and perturbation analyses further show that its predictions rely directly on the ECG input, remain stable under option-order and wording variations, and transfer zero-shot to unseen waveform concepts. Our code is available at https://github.com/zhuoyuanli-lab/ECGJev.

cs.LG · eess.SP

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02040

Multicriteria Recommendation System by Leveraging Predefined, Implicit, and Undefined Criteria

Emrul Hasan, Chen Ding

原文摘要

Data sparsity, a long-standing problem in the recommendation system, occurs when the user-item utility matrix is sparse. For years, many approaches have been experimented with, including utilizing user's textual reviews and multiple criteria ratings. In a review-based recommendation system, user preferences and item attributes are learned from reviews, while in multi-criteria recommendation systems, criteria ratings are used for the recommendation task. Multi-criteria are domain-specific pre-defined explicit criteria and users are bound to provide feedback only on these criteria with discrete values. On the contrary, users are free to express their opinions through textual reviews, which can cover multiple perspectives or aspects. It is possible that these pre-defined, domain-specific explicit criteria are not enough to fully capture the multi-faceted user preferences. To this end, we propose a novel multi-criteria recommendation model that utilizes predefined, implicit, and undefined criteria. We use a semantic similarity-based sentence clustering method to identify the predefined and implicit criteria and a sentiment analyzer to estimate their ratings. The criteria that are extracted from the review and are aligned with predefined criteria are referred to as implicit criteria. A sentence is considered as expressing opinions on an undefined criterion if the similarity score between this sentence and all the predefined criteria is lower than a predefined threshold. Ratings are computed for each extracted implicit criterion and the undefined criterion based on the review content. Finally, we use all three types of criteria and an aggregation model to make the final rating prediction for the recommendation system. Our proposed method demonstrates the superiority compared to several baselines on TripAdvisor and Beer Advocate datasets.

cs.IR

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02037

Scalar Smoothness Is One Point on a Besov Spectrum

Bum Jun Kim, Anastasis Kratsios

原文摘要

Studies of neural network generalization and robustness often summarize smoothness with a single number, such as a Lipschitz constant or a Hessian curvature statistic. However, Lipschitz constants summarize global worst-case sensitivity and Hessian statistics summarize local curvature; neither captures how model responses vary with the size, direction, and location of perturbations encountered in real-world deployment. To measure smoothness under these perturbations, we introduce a finite-difference roughness spectrum that profiles model responses across these conditions. Our roughness spectrum generalizes scalar smoothness measures, bringing Lipschitz sensitivity and directional Hessian curvature into a common framework across perturbation scales. We connect this spectrum to classical Besov theory, which describes how a function's variation changes with scale. Our theoretical analysis also proves that a larger output shift can cause less task damage than a smaller one, and that local curvature alone can miss the blocks most vulnerable to finite perturbations. In image classification, Roughness Source Control (RSC) suppresses margin roughness along noise directions and improves noise robustness in a comparison of 80 checkpoints across four model--dataset combinations. In post-training quantization, we identify blocks that need higher precision by measuring task-loss increases from perturbations matched to the size and direction of each block's quantization error. Our mixed-precision allocation achieves lower perplexity than the Hessian-based HAWQ-V2 baseline under the same GPTQ weights and bit budgets in all 14 settings across seven held-out large language models.

cs.LG · cs.CV

查看论文
arXiv ↗PDF ↗
版本更新 · v6arXiv:2610.07766

OTel: Open Telco AI Datasets, Benchmarks, and Models

Farbod Tavakkoli, Gregory Diamos, Kenneth Church, Roderic Paulk, Ashish Vaswani, David Kanter, Jorden Terrazas, Mark Austin 等 13 位作者

原文摘要

We present Open Telco (OTel), an open telecom AI resource that releases derived telecom datasets for retrieval, reranking, instruction tuning, and safety/abstention, together with 30 full-parameter post-trained baselines spanning 10 embedding models, 3 rerankers, and 17 language models. The community has already engaged substantially with the resource: as of May 3, 2026, the released models have been downloaded over 16 million times and the project has received 157+ pieces of media coverage worldwide. Building on prior open telecom datasets and benchmarks, OTel provides documented telecom data sources, held-out evaluation partitions, trained embedding models, rerankers, context-grounded LLMs, and safety/abstention data in one unified resource. Each baseline starts from an open-weight model and is post-trained on OTel-derived data using an open training recipe, then evaluated on held-out OTel evaluation partitions. OTel post-training improves performance across all three model families: embedding retrieval reaches 93.1% NDCG@10, reranking reaches 0.947 MRR@10, and language-model correctness reaches 87.8%. We release OTel as a reproducible starting point and invite the community to expand the data, improve embedding and reranking models, and build stronger context-grounded telecom LLMs.

cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02034

4D Triangle Splatting Reconstruction of Dynamic Endoscopic Scenes from Monocular Videos

Laura Salort-Benejam, Stamatia Giannarou, Antonio Agudo

原文摘要

Robust 4D reconstruction of deformable endoscopic scenes from monocular videos remains challenging due to non-rigid tissue deformation, illumination changes, occlusions, and unknown camera trajectories. Reliable dynamic reconstruction of endoscopies could significantly enhance visualization, diagnostic accuracy and intraoperative guidance of minimally invasive procedures. We propose SurgicalTS, a self-supervised triangle-splatting framework for dynamic endoscopic scene reconstruction from static monocular cameras. The scene is represented by a canonical set of triangles jointly optimized with a deformation network. We introduce a quaternion-based deformation model, an improved color parametrization, spatio-temporal priors, and a redefined initialization strategy that improves completeness under occlusions. Extensive evaluation on challenging in-vivo endoscopy videos from diverse procedures shows that SurgicalTS successfully models soft-tissue deformations and tool-tissue interactions over time, while consistently outperforming state-of-the-art methods. Our method also reduces training times while maintaining competitive geometric and photometric accuracy results, highlighting the effectiveness of quaternion-based deformation modelling with physical consistency for robust and efficient 4D reconstruction of endoscopic scenes.

cs.CV · cs.RO

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.22850

Accelerating New Product Introduction for Visual Quality Inspection via Few-Shot Diffusion-Based Defect Synthesis

Serkan Hamdi Güğül, Kemal Levi, Burak Acar

原文摘要

Industrial visual inspection systems often suffer from a severe scarcity of labeled defect data, particularly during the early stages of New Product Introduction (NPI). This limitation hinders the deployment of robust supervised detectors precisely when automated quality control is most needed. We present an end-to-end generative framework for high-fidelity, few-shot defect synthesis that enables both in-domain augmentation and cross-domain transfer. Our approach disentangles defect morphology from background appearance by combining masked textual inversion for defect representation learning, noise-blended conditioned generation for surface-aware synthesis, and gradient-aware post-processing for seamless visual integration. We evaluate the framework in two practically relevant settings: few-shot data augmentation, where synthetic samples enrich a small set of real defects, and zero-shot adaptation, where defects learned from a source domain are transferred to a novel target surface without any real target-domain defect examples. Using RF-DETR as the downstream detector, we show that the proposed pipeline substantially narrows the domain gap on a private industrial dataset. In the few-shot setting, synthetic augmentation improves mAP from 78.8% to 83.3%. In the zero-shot setting, synthetic domain adaptation improves mAP from 65.0% to 85.1%. These results demonstrate that high-fidelity defect synthesis can meaningfully accelerate NPI by enabling effective inspection models before sufficient real defect data has been collected.

cs.CV · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02033

How Green Is Your Manifesto? Scoring Environmental Positions in the 2024 EU Elections with an LLM Panel

Franco Corti, Sajid Siraj

原文摘要

Locating political parties on environmental and climate policy is central to the study of party competition, yet the established instruments, expert surveys and the hand coding of manifestos, are costly, periodic, and slow to extend to new documents. We ask whether a panel of large language models can serve as a cheaper and reproducible alternative. Treating each manifesto as a set of sentences, three models from independent providers score every sentence on two dimensions, environmental protection and climate change, separating a party's position on an issue from the salience it gives it, and the judgments are averaged to the manifesto level. Agreement across the models is retained as a built-in reliability signal. We apply the panel to 41 manifestos from the 2024 European Parliament election, covering Austria, France, Germany, Italy, Spain, and the European party families, and validate it against the 2024 Chapel Hill Expert Survey for the 25 parties with a like-for-like expert benchmark, using the other manifestos as diagnostic cases. The panel recovers the expert ordering of parties with strong rank agreement on both dimensions, while reading them as systematically greener than experts do, a gap we interpret as the distance between manifesto promise and governing record. Inter-model disagreement modestly flags where the instrument errs, and position and salience prove related for environmental protection but independent for climate change. The result is a transparent, low-cost, and multilingual instrument that complements rather than replaces expert and hand-coded approaches; the prompt, code, and manifesto-level results are released openly.

cs.CL · cs.CY · econ.GN

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02031

SensOFormer: Foundation Models for Particle Accelerators

Mahindra Rautela, Alexander Scheinker

原文摘要

Large-scale scientific facilities, such as particle accelerators, are complex systems composed of thousands of interacting components that must operate collectively to support diagnostics and control, and ensure safe, reliable operation. These facilities rely on extensive sensor networks that generate large volumes of heterogeneous, incomplete, and noisy telemetry, often sampled at different rates across subsystems. In this paper, we adopt the foundation-model paradigm to learn general-purpose sensor representations from historical accelerator data through self-supervised pretraining. We introduce SensOFormer, a denoising masked transformer with a Perceiver-style encoder-decoder architecture designed to handle variable sensor sets, accommodate heterogeneous sampling rates, and learn robust representations from noisy measurements. The model is pretrained on multiple particle-accelerator datasets and transferred to downstream tasks, including missing-data imputation, anomaly detection, cavity identification, and fault identification. Through extensive evaluations and comparisons, we demonstrate that a single self-supervised model can learn reusable representations for diverse operational tasks in large-scale scientific facilities. Project page: https://mahindrautela.github.io/sensoformer.

cs.LG · physics.acc-ph

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03734

Minimal Superior Penalties: Characterising and Calibrating Decision-Aware Proper Scoring Rules

Rouhollah Ahmadian

原文摘要

Training a classifier means letting a validation metric decide which model is kept and when training stops. A natural requirement on that metric is superiority: every correctly classified forecast should score better than every incorrectly classified one, however close the underlying probabilities are. Neither the Brier score nor the logarithmic score satisfies this on its own. We show that superiority is governed by the separation between two endpoints -- the supremum of the score on correct predictions and the infimum on incorrect predictions -- and derive both in closed form for each score. Their difference is the infimal penalty, the greatest lower bound on the penalty needed to restore superiority. Prior work restores superiority with a penalty calibrated from the first endpoint alone; for the Brier base, our infimal penalty is smaller than that published penalty by a factor ranging from 4.00 at three classes to 2.11 at twenty. We further embed the published and infimal penalties in a one-parameter family and show that, under a single-valued tie convention, every member of the family is strictly proper. In a pre-specified two-dataset case study, mean selected-model macro-F1 rises with the penalty in the reported ten-class regimes, though not distinguishably from noise once seed/selection-mode nesting is accounted for; a fifth regime at forty-seven classes does not reproduce even this descriptive pattern. The intended no-superiority baseline is algebraically a rescaling of the family under test, so the experiment cannot attribute the observed pattern specifically to superiority. When superiority itself is the objective, any penalty strictly above the infimal value suffices; choosing a larger penalty is a separate calibration trade-off rather than a requirement for the guarantee.

cs.LG · stat.ML

查看论文
arXiv ↗PDF ↗