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新论文 · v1arXiv:2611.03930

What Is This Memory Useful For? Measuring Task-Conditioned Utility in Long-Term Agent Memory

Chih-Hsuan Yang, Anjir Ahmed Chowdhury, Feng Yan, Rajeev Thakur

原文摘要

A stored experience may answer one future question and offer little help with another. We measure this task-conditioned utility rather than assigning each memory a single importance score. A task-blind writer stores a memory without seeing the later task; a frozen reader then answers that task with and without the memory, using identical filler. A cost ledger records the resources used to form, store and expose the memory. On single-call factual-recall tasks built from LongMemEval-V2 web-agent memories, the same memory helps more when it contains information needed for the answer. Its mean paired reward gain on these answer-bearing task pairs exceeds that on matched-control pairs by $+0.33$ (95% interval $+0.21$ to $+0.46$). The tested task-blind summaries and structured records yield smaller gains than Raw, including at fixed total context; a budget sweep is consistent with retention of answer-bearing content contributing to the difference. The bearing--control contrast remains positive in a new session with randomized memory position and under a second reader, although individual memory--task labels change. Generic retrieval performs above chance but misses most screen-defined answer-bearing memories. The factual-recall gains do not extend to the tested software-engineering and scientific-coding benchmarks, where even raw memories have no measurable effect. We provide a public, versioned Conditional Memory Utility Dataset of paired measurements and costs. The practical takeaway is to evaluate memory compression by the task utility it preserves alongside its costs. Task-conditioned measurements make this comparison possible; their stability must also be checked.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03928

Sequential auditing of connected forecast errors under partial observations

Yuanbo Zhou, Junjie Xiong

原文摘要

Graph forecasting systems often rely on prediction intervals to quantify uncertainty across connected locations, but the true outcomes needed for evaluation may be only partially observed. We study how an auditor can selectively retrieve missing outcomes to determine whether there exists a connected group whose outcomes violate the same side of their respective forecast intervals. The audit stops when the observed labels determine the event or the query budget is exhausted. On three public graph time series, MOMS reduces mean queries by 19.6% to 38.5% relative to screened random acquisition when 30% of labels are hidden. Under localized masking on METR-LA, shortest witness finds more positive cases within ten queries, although the difference depends on node ordering. Direct component traversal reproduces all 9,696 screened random traces without enumerating connected subsets. The experiments show that reducing queries to a decision does not necessarily improve early event discovery or reduce local computation.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03927

Modular and Efficient Approximate Nearest Neighbors Search with Vectorium and kANNolo

Silvio Martinico, Franco Maria Nardini, Cosimo Rulli, Rossano Venturini

原文摘要

Approximate Nearest Neighbors (ANN) search over neural embeddings powers modern retrieval systems such as Retrieval-Augmented Generation. Embeddings come in three representations (dense, sparse and multi-vector), and efficient search combines an index structure, a similarity function and a quantization scheme. Existing libraries fix one combination, hard-code each one separately, or factor only one side of the problem, and none covers all three representations. We present Vectorium and kANNolo, two open-source Rust libraries that split this design space into a data-management layer and an indexing layer. Vectorium abstracts the vector, the distance function, the quantizer and the dataset independently, and provides 11 encoders, from 32-bit floats to sub-bit codes. On top of it, kANNolo implements Hierarchical Navigable Small-World (HNSW) graph and Inverted Vector File (IVF) indexes that are generic over representation, encoder and metric, and a two-stage index that composes any first-stage index with any rescoring dataset. Since every combination is specialized at compile time, this modularity adds no run-time overhead, and the resulting indexes reach state-of-the-art performance: our Product Quantization matches Faiss in accuracy at competitive speed, our RaBitQ and HNSW match or outperform their reference implementations, and our IVF outperforms Faiss. Against six dense and five sparse systems on seven public collections, our indexes give the best memory-throughput trade-off on dense data in 9 of 12 collection-accuracy pairs and perform well on out-of-distribution queries. On sparse data, the systems built on Vectorium lead on all three collections, with Seismic and SeismicWave the fastest and kANNolo's indexes the most compact.

cs.IR · cs.DB

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.20408

A Cognitive Framework for Idea Diversity in Large Language Models

Yuting Deng, Melanie Brucks, Olivier Toubia

原文摘要

Consumers, marketers, and new product developers increasingly rely on LLMs for ideation. Yet ideas generated by independent LLM sessions tend to be less diverse than ideas generated by independent samples of humans, raising concerns that widespread reliance on LLMs could homogenize marketplace ideation and decrease consumer welfare. We identify two mechanisms undermining LLM idea diversity, informed by theories of creative ideation from new product development and cognitive psychology. First, LLMs aggregate knowledge into a unified distribution rather than exhibiting the knowledge partitioning inherent to human populations, where each person occupies a distinct region of the knowledge space. Second, at the individual level, LLMs exhibit fixation just as humans do, where early outputs constrain subsequent ideation. Two product-ideation experiments demonstrate that targeted prompting interventions can address both mechanisms. Persona modifiers based on ordinary individuals improve knowledge partitioning but amplify fixation. This fixation can be mitigated by using chain-of-thought (CoT) prompting, and combining both strategies produces the highest idea diversity, outperforming humans. These findings offer a theoretically grounded framework for understanding LLM idea diversity and practical strategies for leveraging AI's efficiency without compromising the idea diversity that sustains marketplace variety and consumer welfare.

cs.CY · cs.AI · cs.HC

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2509.07274

LLM Analysis of 150+ Years of German Parliamentary Debates on Migration Reveals a Shift from Post-War Solidarity to Anti-Solidarity in the Last Decade

Aida Kostikova, Ole Pütz, Steffen Eger, Olga Sabelfeld, Benjamin Paassen

原文摘要

Migration has been a core topic in German political debate, from postwar expellee displacement to labor migration and recent refugee movements. Large-scale analysis of such political discourse has traditionally required extensive manual annotation, limiting coverage. Large language models (LLMs) offer a scalable alternative. Using a theory-driven annotation scheme, we examine how well LLMs annotate subtypes of solidarity and anti-solidarity in German parliamentary debates and whether the resulting labels support valid downstream inference. We first evaluate multiple LLMs across model size, prompting strategies, fine-tuning, historical versus contemporary data, and systematic errors. The strongest models, especially GPT-5 and gpt-oss-120B, achieve macro-F1 scores comparable to human agreement, although their systematic errors can bias downstream results. We therefore combine soft-label model outputs with Design-based Supervised Learning (DSL) to reduce bias in long-term trend estimates. Beyond the methodological evaluation, we interpret the resulting annotations from a social-scientific perspective across the Reichstag (1867-1933), West German Bundestag (1949-1990) and Re-Unified German Bundestag (1990-2025) corpus to trace trends in solidarity and anti-solidarity toward migrants, with detailed analysis of postwar Germany (1949-1957) and contemporary Germany (2009-2025). We find relatively high levels of solidarity in the postwar period, especially in group-based and compassionate forms, and a marked rise in anti-solidarity since 2015. We argue that LLMs can support large-scale social-scientific text analysis, but their outputs require rigorous validation and, where systematic errors are present, statistical correction.

cs.CL · cs.CY · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03916

When Latent Dynamics Meet Data Assimilation: A Benchmark of Koopman-Type Representations

Zhiwei Zhang, Wenxuan Yuan, Xiaoyuan Cheng, Pouria Behnoudfar, Chuanqi Chen, Charlotte Moser, Yuta Tarumi, Jin-Long Wu 等 13 位作者

原文摘要

Koopman representations describe nonlinear dynamics through linear evolution in a transformed space, offering an attractive framework for their linear simplicity and generality. Yet these advantages do not necessarily translate into improved performance in deep latent data assimilation (LDA). We systematically compare seven representative methods and structural baselines across five systems spanning approximately periodic to strongly chaotic dynamics and a broad range of state dimensions. We evaluate forecast and assimilation accuracy, physical state recovery, latent-trajectory behavior, and dynamical consistency, and assess Gaussian error assumptions, objective convexity, and uniqueness of analysis solutions. Our results reveal a trade-off between representational flexibility and optimization tractability. Linear evolution of the latent state can stabilize short-term forecasts but restrict expressiveness in strongly chaotic and high-dimensional systems, without consistently improving assimilation accuracy. Parameterized linear operators offer greater flexibility, yet their joint optimization with the encoder--decoder remains nonconvex. Methods with closed-form analysis updates generally perform better and exhibit greater robustness in our experiments, although matrix inversion remains a computational bottleneck. These findings show that the practical benefits of Koopman structure depend on both sufficient observational information and the choice of assimilation procedure.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.16610

Tensor Cookbook: Mastering Tensors through Diagrams

Beheshteh T. Rakhshan, Guillaume Rabusseau

原文摘要

High-dimensional data arise naturally in many areas of science and engineering, including machine learning, signal processing, computational physics, and statistics. Such data are often represented as tensors, multi-dimensional generalizations of matrices. While tensors provide a natural representation for multi-modal structure, their direct manipulation quickly becomes challenging as the order grows: the number of parameters increases exponentially, and algebraic expressions involving many indices become difficult to interpret and implement. Tensor networks (TNs) provide an effective framework for addressing these challenges. Originally introduced by Penrose and developed extensively in quantum physics, the graphical language of tensor networks encodes contractions as edges in a graph, reducing notational overhead and revealing structural properties obscured by index notation. Despite the central role of high-dimensional tensors in modern machine learning and numerical analysis, tensor network diagrams remain underutilized outside quantum computing, partly due to the lack of a self-contained mathematical reference accessible to a broad technical audience. This manuscript provides a self-contained guide to tensor networks and their use in tensor algebra. We present the main operations on tensors, contractions, products, and reshaping through, graphical notation, and show how classical tensor decompositions and related computations are naturally expressed in this framework. We also illustrate how tensor networks simplify the derivation of gradients and the manipulation of high-dimensional probability distributions. Throughout, we show that the diagrammatic approach yields genuinely shorter and more transparent proofs of classical identities, rank bounds, and gradient formulas that would otherwise require laborious index manipulation.

cs.LG · cs.GL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.02258

Universal Conceptual Structure in Neural Translation: Probing NLLB-200's Multilingual Geometry

Kyle Elliott Mathewson

原文摘要

Do multilingual translation models organize meaning around a shared conceptual space, or do they primarily encode language identity and surface form? We probe the encoder geometry of Meta's 600M-parameter NLLB-200 model across 135 languages and 101 Swadesh concepts in contextualized carrier-frame and decontextualized bare-word regimes against null and confound controls. First, two-way variance partitioning reveals an additive language-shift plus conceptual-manifold geometry: language shifts account for 45.7% of variance on bare words vs. 13.7% in context, and mean-centering improves the between-to-within-concept distance ratio (vs. a 0.99 permutation null) by 2.14x on bare words (1.15 to 2.46; 2.27x across disjoint scripts) and 1.19x in context (2.25 to 2.69), with conceptual separation peaking at Layer 10 (mutual k-NN overlap jumping 4.35x). Second, semantic category explains 25.4% of Swadesh convergence variance ($F(7,93)=4.53$, $p=2.21\times 10^{-4}$; partial $F=7.74$ controlling for fertility and length)---over 20x orthography or phonology ($R^2\le 0.012$)---while the advantage of Swadesh over non-loanword concrete nouns ($d=0.54$--$0.69$) is mediated by Zipfian subword compactness ($p=7.43\times 10^{-7}$). Third, language distances track ASJP NLD and LDND phonetic distances within subfamilies under block-permutation and cross-script controls ($ρ_s=0.65$--$0.67$, block $p=10^{-4}$; global bare-word Mantel $ρ_s=0.24$, $p=0.001$), and CLICS$^3$ colexified concepts lie closer across string-distinct, within-category ($ρ_s=0.32$), and bare-word ($d=1.79$) controls ($d=1.43$, $p=1.55\times 10^{-33}$). Finally, basic color terms recover the Berlin and Kay hierarchy ($ρ_s=0.91$--$0.93$, $p\le 2.18\times 10^{-4}$), and semantic offsets exceed distance-matched controls ($p=3.55\times 10^{-5}$, $d=1.06$). We release InterpretCognates, an interactive toolkit and reproducible suite.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.34270

Bayesian Active Learning for Intent Disambiguation in Interactive Robot Planning

Huao Li, Carson Sobolewski, Augustinos Saravanos, William Tan, John Karigiannis, Chuchu Fan

原文摘要

Interactive robot planning requires robots to infer and execute human intentions from natural language instructions that are often ambiguous, incomplete, or underspecified. Although large language models (LLMs) provide a powerful interface for clarification, relying on the generative model to drive an multi-turn conversation can introduce systematic failures. We propose a Bayesian framework that treats clarification as an active learning problem over grounded Signal Temporal Logic (STL) task specifications. Our method uses LLMs to initialize candidate formal specifications and translate informative contrasts into natural-language clarification questions, while Bayesian optimization maintains uncertainty estimation over user intent and selects queries that maximize information gain. After convergence, the inferred STL specification is passed to a formal planner to synthesize a verifiable robot trajectory. Across four simulated and real-world task domains, our approach generally achieves higher task satisfaction and requires fewer clarification rounds than LLM baselines, while helping smaller models close the performance gap against larger reasoning models.

cs.RO · cs.AI · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03899

EpiLoRA: Fusing Many LoRA Adapters into Low-Precision GEMMs for Serving and RL

Hieu Tran, Hong Yu

原文摘要

Low-rank adapters (LoRA) let one base model serve many fine-tuned variants: multi-tenant engines keep a single copy of the base and batch the requests of many tenants, each with its own adapter. As base models move to 8-bit (FP8) and 4-bit formats, their general matrix multiplications (GEMMs) become cheaper, but the adapters do not: engines run them as separate small GPU kernels beside the GEMM, whose latency takes a growing share of every decoding step. An adapter projects its input down to a low rank and back up, and our key observation is that the GEMM needs the down-projection only in its epilogue, the final stage in which it writes its output. EpiLoRA, a plugin for the vLLM engine, therefore launches the down-projection first and the low-precision GEMM as a dependent kernel that streams its weights meanwhile and adds every tenant's up-projection in its epilogue. On NVIDIA Hopper GPUs, EpiLoRA decodes for a single adapter with up to 24% lower time per token than vLLM's fastest configuration, and faster than SGLang and TensorRT-LLM; with many tenants, its adapter kernels deliver more throughput than vLLM's on most GPU-model pairs we tested. The same low-precision base also trains: a LoRA reinforcement-learning trainer that computes on the engine's FP8 weights needs no 16-bit copy, so a 27B model trains on one 48 GB GPU, and an engine that then keeps activations in 16 bits removes most of the remaining mismatch between the model generating training data and the one trained.

cs.DC · cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.00672

ORBIT-FMIB: Tracking Order-Resolved Epistatic Information Through ESM-2

Maryam Rahimimovassagh, Waldemar Karwowski, Ivan Garibay, Niloofar Yousefi

原文摘要

Protein foundation models support mutation-effect and structural prediction, but predictive performance alone does not reveal which forms of biological interaction information remain accessible through model depth. We ask whether ESM-2 retains higher-order epistatic information as strongly as first- and second-order information across its representation hierarchy, introducing ORBIT-FMIB, a diagnostic framework combining Walsh-based interaction decomposition with subset-conditioned neural dependence estimation. The method is validated on synthetic landscapes with known interaction structure before being applied to the dense four-site GB1 fitness landscape using frozen ESM-2 representations. An initial production run suggested ESM-2 retains higher-order epistatic information less well than lower-order information ($Δ_{\mathrm{HO-LO}}=-0.107$). An independent replication of the complete measurement grid, under matched GPU hardware and identical critic seeds, substantially reduced this contrast ($Δ_{\mathrm{HO-LO}}=-0.017$), and its sign was unstable across otherwise-defensible evaluation-pairing choices applied to the same trained critics ($-0.011$ to $+0.015$). We therefore do not currently have robust evidence that ESM-2 selectively loses higher-order epistatic information, nor that retention is equal across orders; the directional question remains open. The measurement protocol itself, including its documented removal of a positional-subset shortcut in pooled critics, remains validated and is unaffected by this finding. ORBIT-FMIB is offered as a diagnostic framework for probing interaction structure in protein foundation models; this study's own replication result illustrates why such probing requires adequately-powered reproducibility checks before its output is treated as a biological finding.

cs.LG · q-bio.QM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.00514

Generate in Reconstruction Space, Match in Semantic Space: Transport Geometry for One-Step Generation

Hugues Van Assel, Edward De Brouwer, Saeed Saremi, Gabriele Scalia, Aviv Regev

原文摘要

Generative modeling and self-supervised representation learning (SSL) optimize structurally different objectives: generative training rewards distributional fidelity, while SSL rewards semantic coherence. Yet recent work repeatedly finds that SSL features improve generative training, though the mechanism of this synergy remains unclear. Here, we study the benefits of SSL in generative modeling in the framework of one-step generation where the role of representation is explicit: frozen SSL features are used to match generated samples to real data. We use the Sinkhorn divergence in that feature space, providing a tractable surrogate for the Wasserstein distance, the population-level discrepancy approximated by Fréchet-style evaluation metrics (such as FID). We find that this objective becomes highly effective when computed in a semantically structured SSL feature space (a 39$\times$ reduction in ImageNet FID). We trace this behavior primarily to matching estimation: semantic SSL features that suppress nuisance reconstruction details induce a more compact geometry, making distribution matching more tractable. As a consequence, the best training SSL features need not match the features used by the evaluation metric. In particular, we show that using Inception as the feature extractor can improve FID while degrading matching stability and sample quality, revealing a form of metric hacking. Using extensive experiments on ImageNet, we identify which SSL feature families lead to best generation performance and show that matching stability is a quantitative criterion for selecting them. Code is available at https://github.com/Genentech/semantic-transport-generation.

cs.LG · cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.02036

Global Coherence: When Every Agent Is Right and the Team Is Still Wrong - A Local-to-Global Semantic Foundation for Multi-Agent Collaboration

Xin Heng

原文摘要

AI agents share code, documents, tools, and analyses. No participant sees the whole state, so every step can look right while the combined result is wrong. We call this the global coherence problem. It is a problem of state, not only of intelligence: a stronger model decides better locally, but cannot see what is missing from its context. The Observation-Aliasing Impossibility Theorem gives the exact condition. An agent can always act validly exactly when every world consistent with its context allows a common action. When look-alike worlds need different actions, no reasoning, role, message, or sample can guarantee the right one; with k such worlds whose valid actions are disjoint, the best randomized policy succeeds with probability 1/k. Finding the smallest sufficient context is NP-hard, but refinement finds one and keeps it current as rules change. A second failure survives full information: valid pieces can fail to fit into one world. The same theorem sets the conditions for coherence; sheaves and groupoids are one way to meet them. Models propose, and the harness decides what is valid. Ten studies test it. On TeamBench, teams overspend a shared budget in 5/5 runs; showing them the live count still leaves 4/5, and a harness that owns the budget leaves 0/5. Identity collisions that no pairwise check can see appear in 23 of 45 public alignment networks; split-view agents create them in 9 of 40 merges, and a network check removes all nine. A frontier LLM solves 40/40 revision tasks when it sees the deciding event; with the event hidden, no arm that reasons longer, votes, or splits into roles significantly beats chance, and a companion study replicates this on models from a second provider. In tau2-bench Telecom, after a silent revert, the harness scores 1.00 against 0.07 for call-time policy checks. Refinement keeps a sufficient context at 28% of the cost of sharing every field.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03891

Hardware-Aware Design and Layout Selection in Quantum Reservoir Computing for Insurance Claims Forecasting

Renáta Rusnáková, Eugen Šlapak, Gaurav K. Pandey, Juraj Gazda

原文摘要

Quantum reservoir computing (QRC) processes temporal information through quantum dynamics, but it remains unclear whether small QRC models can forecast real data competitively with classical models and whether their predictions survive the transfer from ideal simulation to noisy hardware. We address both questions for a six-qubit gate-based reservoir that forecasts next-day motor-insurance claim counts. Five reservoir candidates were selected and frozen using hardware-native interaction graphs, Trotterized Ising dynamics, memory and measurement-robustness criteria, circuit cost, and calibrated-noise screening. On 232 held-out test days, the ideal candidates reached root-mean-square errors (RMSEs) of 5.66-6.12 claims/day, compared with 5.36 for ridge regression. For four of the five, the difference from ridge was not significant under a 95% block bootstrap, and their accuracy was comparable with classical recurrent and reservoir baselines. On the 156-qubit IBM Kingston processor, we ran 63 evaluations in 21 paired rounds, comparing hardware-aware, random-native, and Qiskit optimization level 1 layouts. Hardware-aware placement gave lower RMSE than Qiskit in every round, with a maximum transfer distortion of 7.59 claims/day (11.84 for Qiskit), and avoided the severe transfer failures seen for some random layouts. Most of the hardware distortion appeared as a systematic forecast shift, which a short hardware probe on training inputs detected before the test runs. Longer replay windows raised circuit cost without a consistent gain in forecasting accuracy or hardware transfer. Small QRC models can thus reach competitive accuracy on this task, and hardware-aware layout selection makes their transfer to IBM Kingston more consistent.

quant-ph · cs.LG · q-fin.ST

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03887

The Information Limits of Neural Controlled Differential Equations Under Sparse Observations

Rostislav Gusev, Evgenia Romanenkova, Alexey Zaytsev

原文摘要

Neural controlled differential equations (Neural CDEs) evolve hidden states along continuous paths constructed by interpolating discrete observations. Sparse measurements leave the input between observation times undetermined, and admissible continuous paths consistent with the same data can produce different terminal states. We characterize this ambiguity for planar drivers with bounded speed. The set of compatible terminal states admits a leading-order zonotopic approximation determined by feasible signed areas and transported Lie brackets. Its projection onto a scalar prediction yields a computable, target-specific ambiguity certificate. We also study the number of measurements needed to reduce the remaining uncertainty. For nondegenerate planar CDEs, the optimal worst-case radius over deterministic adaptive sampling policies scales as $N^{-1}$ when speed is bounded and $N^{-2}$ when both speed and acceleration are bounded, where $N$ is the number of observation intervals. Synthetic experiments assess the certificate using admissible continuous completions and examine the role of path regularity.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03885

Self-Consistent Neural Control: Learning Trajectories for Implicit Equilibrium

Hengyi Zhou, Haobo Fang, Xiaonan Huang, Dezhong Tong, M. Khalid Jawed

原文摘要

One big challenge in physical AI is interacting with systems whose states are defined by implicit equilibrium. Learning in these systems is difficult because their responses can be multistable, path-dependent, and sensitive over long horizons. Adjoint learning provides an efficient way to optimize trajectories without unrolling iterative equilibrium solvers, but its gradient relies on the frozen-tangent approximation, which neglects higher-order sensitivity terms and can become inaccurate when the local equilibrium varies rapidly. In this work, we introduce Self-Consistent Neural Control (SCNC), optimizing trajectories along which sensitivity variations are explicitly controlled. We derive a mathematical bound on the resulting gradient error and develop a practical finite-difference approximation. We validate the framework on deformable objects, which are representative of implicit equilibrium systems, ranging from simple quadratic energy models such as elastic rods to complex systems including thin-walled tubes, measuring tapes, and Slinky-like structures. Compared with the adjoint learning and the gradient-free iCEM baselines, SCNC achieves faster convergence and more stable optimization by reducing switching to undesirable equilibrium branches, doubling the stable optimization rate over adjoint learning 83.3% vs. 41.7% and reducing the average loss by more than 1.5 orders of magnitude on thin-walled tubes, where sensitivity variations are pronounced.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03882

ProofPaths: An AI Agent Framework for Assessing Student Reasoning

Mohsen Hariri, Vipin Chaudhary

原文摘要

Students can use large language models to complete assignments, but a correct solution does not establish whether they understand the reasoning behind it. Individualized interviews can probe that understanding but require recurring staff effort. We introduce ProofPaths, an AI agentic framework that combines rubric-based grading with individualized and interactive interviews. Multiple grading agents assess a submission against instructor guidance, exchange critiques, and revise their judgments. Their scores determine which review supplies the grade and feedback. An interview agent asks students to justify steps in their submission and uses their answers to guide follow-up questions. Each question is linked to a retrieved submission excerpt, so instructors can compare the student's explanation with the cited passage. The system tracks assessment progress and stores grading reviews and interview transcripts for each submission. Several deterministic tests of the assessment controllers check source handling, score aggregation, and limits on correction attempts. Instructors can inspect all reviews and transcripts and override grades within the assignment workflow.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03876

Embedding Semantic Entropy: NLI-Free Semantic Uncertainty Estimation with Geometry-Aware Clustering

Akshiya Raveendran, Praveenasarma Baskarakurukkal, Pragatheeswaran Vipulanandan, Kamal Premaratne, Uthayasanker Thayasivam

原文摘要

Large Language Models (LLMs) often generate fluent yet incorrect responses, making un- certainty estimation important for reliable deployment. Semantic Entropy (SE) estimates uncertainty by clustering responses based on semantic equivalence, but it relies on hard entailment-based clustering. We propose Embedding Semantic Entropy, which replaces entailment-based clustering with K-Medoids in semantic embedding space and uses dynamic cluster selection to account for intra-cluster compactness and inter-cluster separation. We evaluate the method on TriviaQA, SQuAD, NQ-Open, SVAMP, and BioASQ using Falcon- 7B-Instruct, LLaMA-2-7B, LLaMA-2-7B-Chat, and Gemma-7B. Results show competitive AUROC and AUARC performance, suggesting that NLI-based entailment clustering can be replaced with embedding-space clustering with limited degradation while improving the inspectability of semantic grouping. This matters because semantic uncertainty scores depend on underlying semantic groups; by making these groups explicit, compact, and separable in embedding space, Emb-SE turns uncertainty estimation from a black-box en- tailment decision into an inspectable clustering problem.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03875

INSIGHT-HPC: An Interpretable System Intelligence for Global HPC Telemetry

Rupak Kadel, Jie Li, Yong Chen

原文摘要

Modern high-performance computing (HPC) clusters integrate multi-generation CPUs and GPUs, which generate rich, high-dimensional telemetry streams from out-of-band baseboard management controllers (BMC), job schedulers, and facility power distribution units. Detecting anomalies within this telemetry is critical to prevent energy waste and costly node failures. Existing approaches often rely primarily on statistical deviations and require stronger labels than actual production environments can provide. In addition, these detectors have a limited operational context for interpreting alerts. We present INSIGHT-HPC, a multi-plane anomaly-detection architecture that combines statistical, workload-aware, and physics-guided evidence into operator-interpretable episodes. INSIGHT-HPC integrates heterogeneous, large-scale telemetry streams and transforms noisy minute-resolution observations into de-duplicated fault episodes with operator-facing explanations. This design allows the system to distinguish workload-explained behavior from genuine infrastructure anomalies while producing interpretable alert summaries for operators. On production HPC telemetry with weak operational labels and controlled HPC-relevant fault injections, INSIGHT-HPC achieves a stronger precision-recall balance than all evaluated baselines. The system achieves a precision of 0.913, a recall of 0.817, an F1 score of 0.862, and an AUROC of 0.896. These results demonstrate the value of combining workload context, physical consistency, and explainable episode-level fusion.

cs.DC · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.23979

SPELUNKA: A Framework for Finding Unanticipated Associations in Vision-Language Models

Cassandra Parent, Isha Agarwal, Kumail Alhamoud, Charikleia Moraitaki, Rafiya Javed, Walter Gerych, Marzyeh Ghassemi

原文摘要

Vision-Language Models (VLMs) are the backbone of many state-of-the-art multimodal systems, but often encode spurious and harmful associations from their training data. While prior work focuses on removing known biases from VLMs, methods for discovering unanticipated associations for retrieval tasks remain underexplored. We introduce SPELUNKA, an automated, inference-time framework for finding unanticipated distributional biases for retrieval tasks in VLMs without pre-specified labeled subgroups, inference-time human supervision, or model fine-tuning. Given a concept of interest, SPELUNKA generates captions for related images, extracts relevant entities from captions, and selects semantically meaningful associations. A frequency-based detection rule then identifies spurious associations by measuring over- or under-representation of these attributes in a VLM's top-ranked outputs relative to their full dataset distribution. We evaluate SPELUNKA across four datasets, and find that it 1) successfully recovers both synthetic and naturally occurring spurious correlations, 2) achieves an average AUROC of 0.93 across four datasets in identifying images strongly associated with a query, and 3) outperforms baselines in finding features that are strongly associated with a given query.

cs.CV · cs.LG

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新论文 · v1arXiv:2611.03872

Learning Almost Complex and Complex Structures

Edward Hirst, Tancredi Schettini Gherardini, Tomás S. R. Silva, Jakob Stein

原文摘要

We study the numerical constructions of almost complex and complex structures on spheres and products of spheres up to real dimension six. Our general set-up parametrises almost complex structures via a global matrix field in an ambient Euclidean space, compressed by the tangent projector. The projector's algebraic residual measures the almost complex condition, while its Nijenhuis tensor is evaluated in the intrinsic norm of the round product metric. Polynomial features and neural networks are trained without prescribed targets to perform general searches for these structures. The almost complex search passes numerical admissibility checks on all nine examples that admit such structures and rejects $S^4$, where a topological obstruction is known. A matrix correction then enforces the almost complex identity, subject to a novel uniform admissibility condition. For integrability, a free polynomial representation with Levenberg-Marquardt optimisation learns the complex structures on $S^2\times S^2$, $S^3\times S^1$, $S^3\times S^3$ and $S^5\times S^1$, with the largest Nijenhuis norms found by adversarial search all below $10^{-9}$. These general searches were unsuccessful on $S^6$, $S^4\times S^2$ and $S^4\times T^2$. Therefore, for these three cases direct numerical reconstructions were built in torus-fibred coordinates. They are integrable by construction, satisfy their compatibility conditions to better than $10^{-11}$, and corroborate the recent constructions on $S^6$ and $S^4\times S^2$.

math.CV · cs.LG

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版本更新 · v8arXiv:2508.01415

RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Interactive Environmental Learning in Physical Embodied Systems

Mingcong Lei, Honghao Cai, Yuyuan Yang, Yimou Wu, Jinke Ren, Zezhou Cui, Liangchen Tan, Junkun Hong 等 19 位作者

原文摘要

Long-horizon embodied tasks require agents to learn, reason, and generalize robustly across complex real-world environments. However, existing approaches often struggle with partial observability, fragmented spatial reasoning, and inefficient integration of heterogeneous memories, limiting their capacity for long-horizon adaptation. To address this, we introduce RoboMemory, a brain-inspired framework that unifies Temporal, Spatial, Semantic, and Episodic memory for efficient long-horizon planning and interactive learning. Its core innovations are a comprehensive memory system designed for embodied tasks, and a dynamic spatial knowledge graph for scalable, consistent memory updates. Extensive experiments on EmbodiedBench show that RoboMemory improves the average success rate of Qwen2.5-VL-72B-Ins by 26.5 percentage points and achieves performance comparable to strong closed-source VLM agents. Real-world trials further suggest RoboMemory's potential for interactive environmental learning by reusing accumulated experience across repeated task attempts. These results demonstrate RoboMemory as a scalable memory-augmented framework for embodied agents, improving long-horizon planning and cumulative learning in complex interactive environments.

cs.RO · cs.AI

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新论文 · v1arXiv:2611.03868

EvoGroup: A Self-Evolving Harness for Group Agents in Shared Contexts

Xinyi Xu, Xiaoyang Li, Yucheng Feng, Wenqing Chen, Youliang Yuan, Wei Shi, Rongxiang Weng, Zibin Zheng

原文摘要

With the rise of Group Agent such as Claude Tag, agents increasingly serve as digital workers for long-term team collaboration. These agents operate over shared contexts that continuously accumulate through multi-user interactions. However, existing agent memory systems are mainly designed for individual users and struggle to capture group-level knowledge in shared contexts, including group relationships and collaboratively formed knowledge. Manual memory strategies further limit adaptation to diverse and evolving shared contexts. To this end, we propose \textbf{EvoGroup (EvoG)}, a self-evolving harness for group agents that starts from a minimal six-tool cold start and adapts through interaction with shared context. EvoG uses confidence and task outcomes to select informative trajectories, while also eliciting self-reflections on uncertainty in low-confidence cases. The analysis agent then examines selected trajectories through adaptive progressive disclosure, followed by bucketed analysis to synthesize high-level findings that guide the evolve agent in revising the Harness. Empirically, pass@1 on held-out split of EverMemBench improves from 71.07\% to 89.58\%, surpassing both self-evolving memory and human-designed harnesses, including Claude Code (85.83\%). To evaluate generalization, we freeze the evolved harness and evaluate it on the unseen GroupMemBench, where, with GPT-5.5, it demonstrates effective generalization and outperforms both memory baselines and human-designed harnesses.

cs.CL

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新论文 · v1arXiv:2611.03867

ExaTune: Exhaustive Hyperparameter Landscape Exploration at Scale

Kevin Dick, Katie Lonsway

原文摘要

Hyperparameter optimization (HPO) returns a single optimal configuration, systematically discarding the explored performance landscape that encodes how results depend on configuration choices. Fitness landscape analysis comprises a robust collection of methods from evolutionary computation offering ruggedness indices, autocorrelation functions, and fitness-distance correlation that have never been applied to machine learning hyperparameter spaces at scale, given that fine-grained ground-truth enumeration data has not existed. We present ExaTune, an open-source HPC system to produce a new artifact class: the hyperparameter landscape map. ExaTune exhaustively evaluates every point on a discretized hyperparameter grid via $K$-fold cross-validation, aggregates results into reproducible parquet benchmarks, and applies a suite of five formalized landscape metrics: spatial autocorrelation (Moran's $I$), ruggedness, local optima density, fitness-distance correlation (FDC), and variance-based parameter importance. We release a benchmark collection of 8 complete landscape maps (totalling 285,080 exhaustively evaluated configurations) spanning five model families (SVMs, random forests, XGBoost, ridge regression, and $k$-nearest neighbors) across six classification and two regression benchmarks, hosted on HuggingFace. Empirical analysis demonstrates that Bayesian optimization, Optuna, Hyperband, and Random Search at standard trial budgets systematically miss instability regions, secondary optima, and parameter interaction effects that are visible in complete landscapes; information that is structurally unavailable to any optimizer by design.

cs.LG

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新论文 · v1arXiv:2611.03862

Dr. No: Feature Steering for Abstention in Vision-Language Models

Farid Hasanov, Riccardo Renzulli, Bahram Zonooz, Mykola Pechenizkiy, Decebal Constantin Mocanu

原文摘要

Large foundation models often answer even when the available visual or textual evidence is insufficient to support a reliable conclusion. This failure to abstain makes them hard to trust in high-stakes or information-seeking settings, where a confident but unsupported answer can be worse than no answer at all. It also raises a natural question: can the internal mechanisms behind this behaviour be identified and controlled at inference time? We present Dr. No, a feature-steering approach that mines sparse autoencoder (SAE) features associated with abstention and commitment, then modulates a small set of them during the prefill pass, without retraining the base model. Dr. No is evaluated on LLaVA-1.6 Mistral 7B across multimodal abstention benchmarks, including UNK-VQA and MM-UPD, and additionally benchmarked on language reasoning tasks using Qwen3.5-2B on AbstentionBench subsets. Against multiple uncertainty-based abstention baselines, feature steering achieves a more favourable balance between correct answering and appropriate abstention than post-hoc rejection methods. The gains are strongest on UNK-VQA and on MM-UPD cases involving image--question or question--answer incompatibility, and transfer most clearly to mathematical reasoning subsets of AbstentionBench. However, performance is more limited on absent-answer detection and retrieval-heavy multi-hop QA, such as MuSiQue. These results suggest that sparse internal features provide lightweight control handles for shaping uncertainty-aware behaviour in foundation models, while also showing that abstention control is source-dependent rather than governed by a single universal signal.

cs.LG

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新论文 · v1arXiv:2611.03861

Predictive gains, causal losses: why interventional competence cannot be learned from data alone

Marcus Thomas

原文摘要

Retaining predictive gains can require persistent errors in a model's response to interventions. For deterministic predictors with finite second moments under squared-error loss, any observational advantage over the best predictor using only an outcome's direct causes requires dependence on other inputs, regardless of the learning rule. When admissible interventions vary inputs while preserving the worldly outcome, this dependence yields a positive lower bound on root-mean-square intervention-response error. We identify conditions under which population gradient updates introduce this dependence from a parent-only baseline or damage a previously correct mechanism through shared parameters while improving other predictions. These dependencies can also make model behavior vary with how the same intervention is realized. We characterize interventional alignment through a common output mapping across internal states and physical processes realizing the same intervention target and relevant context. Correction is limited by the causal hypotheses currently admitted within inquiry. The relevant hypothesis class comprises these causal alternatives, rather than the function class the computational system could in principle represent. For predictions restricted to these alternatives, interventional mean error cannot fall below the best approximation they permit, whether evidence is observational or interventional. Reducing error below a positive limit requires admitting better hypotheses, which may entail revising the causal variables, mechanism structure, or intervention interpretation. Interventional criticism constrains replacements without determining them. We conjecture that reliably acquiring and maintaining interventional competence when causal commitments fail requires autonomous participation in proposing alternatives, testing their consequences, and revising them in response to error.

cs.AI · cs.LG

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版本更新 · v2arXiv:2610.10942

StoreBench: A Live-Commerce Environment for Evaluating and Training Autonomous Operator Agents

Daksh Raghuvanshi, Ved Vedere, Yifan Wang

原文摘要

Reinforcement learning environments are now a primary lever for improving large language model (LLM) capabilities in post-training, yet most agentic benchmarks remain static: the world moves only when the agent acts, the reward is a terminal verdict, and the pass bar is set arbitrarily. We introduce StoreBench, a live-commerce environment in which an agent runs a mid-size online apparel store on a production-grade commerce backend, testing long-horizon planning and economic judgment under uncertainty. Customers order around the clock, suppliers reprice and fail, and market shocks arrive with partial or no warning. The agent acts through the same 29 merchant tools a human operator would use, under a windowed operation budget that makes simulated time a function of actions taken, so model latency cannot influence simulated time. Pass thresholds are calibrated against scripted anchor policies, the reward is hardened against a catalogue of reward hacks, and every episode replays identically given a sequence of actions. We evaluate seven frontier LLMs on 11 scenarios of 30 to 45 days and a full simulated year, over three world seeds at matched reasoning effort. No model matches the scripted smart-triage policy on average: the best, DeepSeek-V4-Pro, passes 49% of task-seed cells against the heuristic's 97%. Human experts working through the same tools and budgets outscore every model (mean composite 0.708 vs. 0.700). Over a full simulated year under the Claude Code harness, most models show dramatic performance improvement. In a GRPO post-training run, Qwen3.5-27B trained on only five disjoint tasks raises its mean composite on the held-out evaluation tasks from 0.136 to 0.373. We release five example training-split tasks, ten sample trajectories, and the scoring and verification tooling; the full environment and evaluation suite are withheld to keep the benchmark uncontaminated.

cs.AI · cs.CL · cs.LG

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版本更新 · v2arXiv:2605.15999

Trajectory and Morphology Planning for Morphing Quadrotors in Ultra-Narrow Passages using Limited Perception

Harsh Modi, Xiao Liang, Minghui Zheng

原文摘要

This paper presents an online trajectory and morphology planning framework for morphing quadrotors to traverse passages narrower than their unfolded width using only a 2D LiDAR for obstacle sensing. Fixed-frame vehicles cannot pass through such gaps, and classical artificial potential field (APF) based obstacle costs seal them off for planners as well, because the repulsive cost grows sharply inside a tight gap. The core enabler of our framework is a novel cost reduction factor, computed in closed form from the LiDAR data, that cancels the obstacle cost along the medial axis of a narrow passage. A true opening thus becomes a low-cost corridor, while the full repulsive cost is retained everywhere else. The cost reduction factor is embedded at every prediction node of an acados-based nonlinear MPC, which jointly plans the trajectory, yaw, and fold angle of the platform. A user-specified minimum-width limit ensures that the cost reduction factor only opens the corridors that the platform can actually pass. Gazebo/PX4 simulations and hardware experiments on the MorphoCopter demonstrate autonomous folding and unfolding through curved, inclined, and cluttered passages narrower than the unfolded width, where the standard repulsive cost fails to pass safely in most of the trials. The implementation code is available at \href{https://github.com/harshjmodi1996/morphocopter_mpc}{\textcolor{blue}{Github}} and a short video about the paper is available at \href{https://zh.engr.tamu.edu/wp-content/uploads/sites/310/2026/10/Morphocopter_planning_final.mp4}{\textcolor{blue}{\textbf{video link}}}.

cs.RO · eess.SY

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版本更新 · v3arXiv:2507.01216

Eco-MobiLLM: Efficient LLM Fine-Tuning on the Mobile Device via Server Assisted Side-Tuning

Xingke Yang, Liang Li, Zhiyi Wan, Sicong Li, Xiaoqi Qi, Jiang Liu, Tomoaki Ohtsuki, Xin Fu 等 9 位作者

原文摘要

There is a huge gap between numerous intriguing applications fostered by on-device large language model (LLM) fine-tuning (FT) from fresh mobile data and the limited resources of a mobile device. While existing server-assisted methods (e.g., split learning or side-tuning) may enable LLM FT on the local mobile device, they suffer from heavy communication burdens of activation transmissions. In addition, their multiple on-device forward passes over the same samples across training epochs lead to substantial iterative computation on the mobile device. To address those issues, we develop Eco-MobiLLM, an efficient LLM FT method which can be deployed on the mobile device via server-assisted side-tuning. To reduce communication cost, Eco-MobiLLM develops an activation shortcut that transmits only the tokens involved in the loss calculation instead of full activation matrices to guide the side network tuning. Besides, to improve computing efficiency and further accelerate FT convergence, Eco-MobiLLM introduces a novel two-stage training paradigm, which allows the server to reuse historical activations and saves the mobile device from repeatedly computing forward passes for the recurring data samples. Extensive experimental results demonstrate Eco-MobiLLM's superiority.

cs.LG · cs.CR

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新论文 · v1arXiv:2611.03853

Performance, Scalability and Maintenance: An Experimental Study of RPA vs. Autonomous Agents in the Healthcare Domain

Daniel Schulz, Pedro Cabral Buchaim, Bernardo Soares, Fernando Rafael Ensslin Schwengber, Lorenzo Farias, Daniela Duarte da Silva Bagatini, Ana Paula Wernz da Cunha Müller

原文摘要

Healthcare information systems are fragmented, and retrieving data from legacy web portals is a recurrent need. Robotic Process Automation (RPA) is deterministic but sensitive to interface changes, whereas autonomous agents based on large language models (Agent Process Automation, APA) promise adaptability at a higher computational cost. Following Design Science Research, we developed and compared an RPA solution (Playwright) and an APA solution (browser-use, LangGraph and Gemini 2.5 Flash-lite) to retrieve mammography and breast ultrasound reports from a laboratory portal, treating reports solely as informational artifacts. Across two rounds (T and T+7), RPA reached 98.90% accuracy and 98.27% F1-score versus 96.49% and 94.48% for APA, was about 13 times faster (16.07 s vs. 207.21 s per patient) and cost about six times less per report (USD 0.030 vs. 0.187). Both reached 100% step success rates; APA varied more over time. For a stable interface, RPA was superior.

cs.AI · cs.SE

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