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新论文 · v1arXiv:2611.11317

Understanding Spike Encoding across IoT Sensing Modalities: An Empirical Study

Yuxuan Hou, Di Yu, Linshan Jiang, Jin Zhang

原文摘要

Spiking neural networks (SNNs) offer a path to energy-efficient sensing on Internet of Things (IoT) devices. The input encoder shapes that opportunity by changing the cues available for recognition and the work required to process them. We study these choices across wireless, inertial, surface electromyographic, and tactile sensing. For wireless activity recognition, interleaving reduces estimated energy for arithmetic in the network layers, latency from the prepared input window to returned scores, and energy on the graphics processing unit and system-on-chip (GPU/SoC) rail, compared with repeating the full window on Jetson AGX Orin. With the same input size at each SNN time step, repeating a compact input can yield higher mean development accuracy than interleaving. The inertial experiments show that higher accuracy can come at higher device cost. Direct input can also be faster and more accurate than an event encoder while using less energy on the measured rail, even when its estimated energy for arithmetic in the network layers is higher. Level crossings improve grasp recognition on a later day for previously observed users. In tactile slip detection, level crossings shift the balance between missed slips and false alarms, and checkpoint selection can reverse which input offers higher slip recall. We use these findings to outline how edge sensing systems can profile encoders against the signal structure, the errors that matter to the task, and the cost of the full inference pipeline.

cs.NE

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.14477

Test-Time Learning with an Evolving Library

Weijia Xu, Alessandro Sordoni, Chandan Singh, Zelalem Gero, Michel Galley, Xingdi Yuan, Jianfeng Gao

原文摘要

We introduce EvoLib, a test-time learning framework that enables large language models to accumulate, reuse, and evolve knowledge across problem instances without parameter updates or external supervision. Instead of adapting model parameters, our approach maintains a shared library of knowledge abstractions, including modular skills and reflective insights, automatically extracted from the model's own inference trajectories. To support continual improvement, we introduce a principled weighting and consolidation mechanism that jointly optimizes for immediate utility and long-term value. This allows simple, instance-specific abstractions to evolve into more general and reusable ones over time. Across challenging benchmarks in mathematical reasoning, code generation, and multi-turn agentic environments, EvoLib improves substantially over the top test-time scaling and learning methods without ground-truth feedback.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08578

HaNDF: Object-Conditioned Geometric Neural Hand Distance Fields

Zuhao Liu, Melih Darcan, Zhengdi Yu, Riza Alp Guler, Tolga Birdal

原文摘要

We propose, HaNDF, object-conditional neural distance fields as data-driven priors for modeling the plausible hand-object configurations, in the product manifold of hand articulations. While recent unconditional generative priors showed success in modeling human pose, hands are versatile articulated bodies often found in interaction with other objects. This makes it crucial to develop a generative prior which can model the plausible hand poses conditioned on the object under interaction. To this end, our HaNDF proposes a geometry-aware, object-conditioned neural distance field, representing the plausible articulations in the zero-level set of a conditional field. We leverage tools from Riemannian geometry to (i) represent plausible hands in the zero level-set of an object-induced neural field, and (ii) project a given pose onto this field. Using HaNDF, we can also optimize for a plausible condition, determining the object under interaction. Our extensive evaluations demonstrate that our HaNDF provides a flexible and powerful prior for hand-object interaction, suitable for applications such as pose refinement, reconstruction under occlusion, and physically plausible manipulation synthesis.

cs.CV · cs.AI · cs.LG · cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2505.09338

Llama See, Llama Do: A Mechanistic Perspective on Contextual Entrainment and Distraction in LLMs

Jingcheng Niu, Xingdi Yuan, Tong Wang, Hamidreza Saghir, Amir H. Abdi

原文摘要

We observe a novel phenomenon, contextual entrainment, across a wide range of language models (LMs) and prompt settings, providing a new mechanistic perspective on how LMs become distracted by ``irrelevant'' contextual information in the input prompt. Specifically, LMs assign significantly higher logits (or probabilities) to any tokens that have previously appeared in the context prompt, even for random tokens. This suggests that contextual entrainment is a mechanistic phenomenon, occurring independently of the relevance or semantic relation of the tokens to the question or the rest of the sentence. We find statistically significant evidence that the magnitude of contextual entrainment is influenced by semantic factors. Counterfactual prompts have a greater effect compared to factual ones, suggesting that while contextual entrainment is a mechanistic phenomenon, it is modulated by semantic factors. We hypothesise that there is a circuit of attention heads -- the entrainment heads -- that corresponds to the contextual entrainment phenomenon. Using a novel entrainment head discovery method based on differentiable masking, we identify these heads across various settings. When we ``turn off'' these heads, i.e., set their outputs to zero, the effect of contextual entrainment is significantly attenuated, causing the model to generate output that capitulates to what it would produce if no distracting context were provided. Our discovery of contextual entrainment, along with our investigation into LM distraction via the entrainment heads, marks a key step towards the mechanistic analysis and mitigation of the distraction problem.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.13284

Tight Bounds for $(1+ε)$-Approximation for Minimum Spanning Trees in Doubling Metrics

Jan Höckendorff, Felix Hommelsheim, Christian Sohler, Di Yue

原文摘要

The minimum spanning tree (MST) problem is one of the most basic optimization problems on metric spaces and graphs. We study the problem of computing a $(1+ε)$-approximation to the MST of an $n$-point metric space of doubling dimension $\mathrm{ddim}$. In doubling metrics, previous deterministic algorithms incur a running time with dependence $ε^{-O(\mathrm{ddim})}$. We give a deterministic algorithm that computes a $(1+ε)$-approximate MST in time $$ O_{\mathrm{ddim}}\left(n\log n+ \min\left\{n^2,\frac{n}ε\log(1/ε)\right\}\right) $$ in the standard real RAM model with a unit-cost exact-distance oracle. For bounded doubling dimension, this improves the previous dependence on $ε$ to essentially linear in $ε^{-1}$. We complement this algorithm with a lower bound of $$ Ω\!\left(n\log n+ \min\left\{n^2,\frac{n}ε\log(1/ε)\right\}\right) $$ on running time in the same model, already for metrics of doubling dimension at most seven, showing that our running time is essentially tight. For points in $\mathbb R^d$ with $d = O(1)$, exploiting Euclidean geometry allows us to further improve the dependence on $ε$. We obtain a $(1+ε)$-approximate MST in expected time $$ O_d\left( n \log n + \min\left\{n^2, \frac{n}{ε^{1 - β_d}} \log^{1-β_d}(1/ε) \right\} \right) $$ for some $β_d=Θ(1/d)$, improving the accuracy-dependent term $nε^{-2}\log^2(1/ε)$ of Arya and Mount [SODA'16].

cs.DS

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.65ACL Anthology · ACL 2026

ACRM: Multi-Agent Trajectory Learning for Automated Credit Risk Model Refreshing in Production

Liangzu Liu, Mengzhe Ruan, Xiaotian Chen, HaonanChen, XudongNiu, Wendi Yuan, YuechenLi, Yang Liu 等 9 位作者

原文摘要

Credit risk models suffer from rapid performance decay due to distribution shifts, requiring frequent updates to meet strict operational guardrails. However, manual refreshing takes weeks of trial-and-error across upstream data engineering and downstream training. We present ACRM, a deployed multi-agent framework that automates the end-to-end credit modeling workflow by treating it as a learnable trajectory of agent interactions. Unlike AutoML, which optimizes hyperparameters on fixed datasets, ACRM’s action space extends to upstream data semantics—cohort selection, observation windowing, feature screening—where the majority of performance recovery occurs. A central Orchestrator coordinates specialist agents through a three-stream decision stack: rule-based safety guardrails, retrieval-augmented grounding from historical workflows, and preference alignment via DPO on expert-labeled trajectories. Deployed at a major fintech institution for three months across six business scenarios, ACRM reduced the average model refresh cycle from weeks to 1.1 days and iteration rounds by 65%, while maintaining superior stability metrics.

cs.CL · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表