Search: “llm”

ChronoScribe: Semantic-Numerical Specialization for Instruction-Driven Time Series Forecasting

arXiv:2610.16345v1 Announce Type: cross Abstract: Recent advances in instruction-driven time series forecasting highlight a fundamental tension: systems must simultaneously master natural-language ins

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Fast Quantized Johnson-Lindenstrauss Transforms

DGX agent

arXiv:2610.16289v1 Announce Type: new Abstract: Quantization has emerged as one of the primary techniques for reducing the memory footprint of large language models during inference. A prominent examp

researcharxiv-cs-lg

14 Oct 2026

GeoMotionGPT: Geometry-Aligned Motion Understanding with Large Language Models

arXiv:2601.07632v5 Announce Type: replace Abstract: Discrete motion tokenization has recently enabled Large Language Models (LLMs) to serve as versatile backbones for motion understanding and motion-l

safetyarxiv-cs-cv

Information-Theoretic Foundations of Online Temperature Sampling

arXiv:2610.15043v1 Announce Type: new Abstract: Repetitive, degenerate generation is a persistent failure of LLM decoding, and the standard remedies (temperature, nucleus, and top-k sampling) are stat

Towards Safe Autonomy for Computer Use

arXiv:2610.18508v1 Announce Type: cross Abstract: The safety of LLM-based agents currently rests on alignment training and safety classifiers, neither of which can rule out that a sophisticated indire

safetyarxiv-cs-lg

Understanding the Interplay between Memorization and Learning in Large Language Models

arXiv:2507.14777v2 Announce Type: replace Abstract: We investigate foundational questions about the interplay between memorization and learning in large language models (LLMs). When an LLM generates a

researcharxiv-cs-lg

You can't solve real-world physical tasks through autoregressive sampling alone. The physical world is entirely unforgiving to hallucination…

agentsfrancois-chollet--x

You can't solve real-world physical tasks through autoregressive sampling alone. The physical world is entirely unforgiving to hallucination or approximate pattern matching. If we want actual autonomo

Applications

wait so this feels a bit different too in a good way

applicationsyohei-nakajima--x

wait so this feels a bit different too in a good way You already gave the LLM the answer. So why is it writing it out from scratch? Introducing a new AI model architecture: Constrained Decoding Models

13 Oct 2026

Agentic Peer-to-Peer Networks: From Content Distribution to Capability and Action Sharing

arXiv:2603.03753v2 Announce Type: replace-cross Abstract: The ongoing shift of Artificial Intelligence (AI) from centralized cloud application programming interfaces (APIs) to local inference on edge

CLEAT: Learning Value-Guided Clarification for Interactive Agents

arXiv:2610.12655v1 Announce Type: new Abstract: LLM agents can execute increasingly complex tasks, but effective assistance also requires aligning their actions with goals and constraints that users m

safetyarxiv-cs-ai

DP-MIND: Differentially private memory-guided Long context Inference

arXiv:2610.13132v1 Announce Type: new Abstract: LLM-as-a-Service has become a dominant paradigm for accessing large language models without requiring local deployment. However, the process raises seve

GeoGR: Spatial-Semantic Token Compression and Group Routing for Efficient 3D VLMs

arXiv:2610.13035v1 Announce Type: cross Abstract: Multi-view representations enable pretrained image and video vision-language models to understand 3D scenes, but their long visual token sequences mak

Let Large Language Models Judge Each Other: Multi-Agent Peer-Reviewed Reasoning for Medical Question Answering

arXiv:2606.15419v2 Announce Type: replace-cross Abstract: Objective: To enhance the accuracy, interpretability, and robustness of large language models (LLMs) in medical question answering (MedQA). Me

Same Query, Different Answer: Run-to-Run Variance in Semantic Query Processing

arXiv:2610.13823v1 Announce Type: cross Abstract: Semantic query engines ask a large language model (LLM) to judge individual rows, and benchmarks compare these engines on the quality of a few executi

Temporal Evidence Forests for Long-Horizon Multi-Party Conversational Memory

arXiv:2610.12874v1 Announce Type: new Abstract: Long-term memory has been widely adopted in LLM-based agents to support persistent interaction and personalized assistance. Long-term multi-party intera

EvoAlloc: A Self-Evolving Resource Allocation Agent for Efficient Program Evolution

arXiv:2610.12086v1 Announce Type: new Abstract: LLM-based program evolution relies on evaluation feedback to guide the iterative search for high-performing programs. However, evaluation is often compu

agentsarxiv-cs-ai

9 Oct 2026

TRACE: Diagnosing Verifier Brittleness in Agentic Evaluation

arXiv:2610.11678v1 Announce Type: new Abstract: Verifier scores now serve as both benchmark metrics and training rewards for large language model (LLM) agents, and a change in score is routinely read

9 Oct 2026

2 months (meme-d) research, Do People Notice the Difference Between AI Models?

model-releasesr-localllama

Preamble and disclaimer, Sample size: 8, at any size of form this research is just screwing around being writing it down. This was just a little,(well... big), curiosity test I wanted to run to see wh

Certified by Abstention: Distribution-Free Guarantees for Chain-of-Thought Verifiers at Small Calibration Budgets

arXiv:2610.09541v1 Announce Type: cross Abstract: Signals that predict whether a chain-of-thought (CoT) trace is correct are compared by AUC, but deploying one requires a threshold with a guarantee. W

Constrained-Action AI Remediation for SIEM/XDR via a NeMo-Guardrails Proxy

arXiv:2610.09906v1 Announce Type: cross Abstract: Security Operations Centers (SOCs) for information technology and operational technology share one incident-response problem: a flood of correlated al

safetyarxiv-cs-cl

I would rather quit NLP than read another paper like this: The rise of antithesis in NLP papers

arXiv:2610.10092v1 Announce Type: new Abstract: For better or worse, LLMs are by now used routinely for scientific writing.footnote{This paper is no exception; we did use AI to assist with writing som

researcharxiv-cs-cl

sk-bench: A Native-First Benchmark for Evaluating Large Language Models in Slovak

arXiv:2610.09152v1 Announce Type: new Abstract: Multilingual LLM benchmarks omit Slovak, a morphologically rich West Slavic language of five million speakers, or cover it only by machine translation.

Talking with Language Models

arXiv:2610.09064v1 Announce Type: cross Abstract: When we interact with large language models (LLMs), are we having a conversation? They are designed to invite us to treat them as intelligent interloc

researcharxiv-cs-cl

The Confidence Game: Strategic Miscalibration in Human-AI Delegation

arXiv:2610.09371v1 Announce Type: cross Abstract: Calibrated uncertainty quantification is essential to ensuring AI agents are trustworthy and reliable. However, when agents seek to maximize user enga

agentsarxiv-cs-cl

The Long Road to the Same Answer: Cognitive Bias Under Escalating Reasoning Budgets in Large Language Models

arXiv:2610.10049v1 Announce Type: new Abstract: Reasoning models allocate extra computation at inference time and present their answers as the product of deliberate thought. If this deliberation works

Which Language Should a Skeleton Speak? Language Choices in Multilingual Reasoning

arXiv:2610.09607v1 Announce Type: new Abstract: Skeleton-based reasoning prompting is a promising training-free approach for structuring LLM reasoning, but prior work largely assumes an English-centri

COMPASS: Finding Where Reasoning Lives in Language Models

arXiv:2610.07469v1 Announce Type: new Abstract: Explicitly eliciting reasoning substantially improves LLM performance. Existing approaches require a predefined characterization of reasoning, whether t

DAEDALUS: Bootstrapping Agent Memory from Self-Generated Tasks

arXiv:2610.08048v1 Announce Type: new Abstract: LLM agents often lack the operational knowledge to act reliably in new environments, as they must discover specific tool behaviors or environment conven

Incidental information contaminates patient notes and disrupts clinical reasoning in large language models

arXiv:2610.08585v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly relied upon to support ambient documentation and clinical reasoning. Here we examine the impact of a failu

researcharxiv-cs-cl

Applications

Memory Depth and Reconstructed Context Width: A Controlled Evaluation of Hierarchical Retrieval

arXiv:2610.08300v1 Announce Type: new Abstract: Long-term conversational memory is becoming an integral component of modern LLM systems. Proposed architectures group records by topics and events, cons

applicationsarxiv-cs-cl

OntoPlan: An Ontology-Grounded Scene Representation and Agentic Framework for Scalable Robot Task Planning

arXiv:2610.07649v1 Announce Type: new Abstract: Large language model (LLM)-based robot task planning is promising for open-ended instruction following, but degrades on long-horizon tasks in large envi

agentsarxiv-cs-ro

Principles that Guide, Actions that Inform: Agent Evolution via Knowledge Abstraction

arXiv:2610.06964v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated strong capabilities in interactive environments, yet their ability to continually evolve from experi

PuzzleJAX: A Benchmark for Reasoning and Learning

arXiv:2508.16821v2 Announce Type: replace Abstract: We introduce PuzzleJAX, a GPU-accelerated puzzle game engine and description language designed to support rapid benchmarking of tree search, reinfor

SkillPoison: Progressive Skill Poisoning via Successful Experiences

arXiv:2610.07645v1 Announce Type: cross Abstract: Self-improving LLM agents increasingly distill successful experiences into persistent, reusable skills. Existing skill attack methods corrupt this lea

SquidAgent: Parallelize Wisely, Coordinate Efficiently

arXiv:2610.08647v1 Announce Type: new Abstract: LLM-based agents solve complex multi-step tasks, but sequential execution incurs substantial latency. In principle, parallelizing work across multiple a

Stepped MoE: Segment-Level Routing with Configurable Inference Complexity

arXiv:2610.07348v1 Announce Type: cross Abstract: Training large language models (LLMs) is resource-intensive, and adapting them for diverse deployment scenarios with varying computational constraints

WorkflowOps: Learning Agent Collaboration Priors for Multi-Agent Workflow Orchestration

arXiv:2610.07860v1 Announce Type: new Abstract: Multi-agent systems are increasingly deployed for complex knowledge work, yet their orchestration layers remain largely memoryless: each new task is dec

agentsarxiv-cs-ai

Bayes-Sufficient Compression Is Not Enough: How Does Communication Help Multi-Agent Systems?

arXiv:2610.03769v1 Announce Type: new Abstract: Multi-agent LLM systems pair a sender with broad context and an executor with a limited local view. We study when a short message improves the executor'

How Execution Assumptions Change Short-Horizon Sharpe Rankings: Evidence from a Synthetic Trading Benchmark

arXiv:2610.05077v1 Announce Type: new Abstract: Backtests of LLM trading agents often assume that every order fills at the closing price. We ask whether this choice changes only reported returns or al

Request Order Matters: Cache-History Sensitivity in Selective KV-Cache Reuse for Rolling Agents

arXiv:2610.05833v1 Announce Type: cross Abstract: Long-running agents repeatedly call an LLM while retaining most of their document window, evicting old documents, and appending new ones. These rollin

safetyarxiv-cs-lg

ROOT: Discovering Rewards for User-Specified Embodied Behaviors

arXiv:2610.04250v1 Announce Type: new Abstract: Reinforcement learning for embodied control remains constrained by the difficulty of reward specification. Although recent large language model (LLM)-ba

safetyarxiv-cs-ro

SALUS: Automated Auditing of NL-to-SQL Benchmarks through Weak Supervision of Multi-Agent Output

arXiv:2610.05540v1 Announce Type: cross Abstract: Natural language to SQL (NL-to-SQL) benchmarks are foundational to progress in data analysis research, yet recent work has shown that widely-used benc

Self-Reflection Fine-Tuning: Enhancing Agent Security against Prompt Injection Attacks from Failure Experience

arXiv:2610.04269v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed in tool-augmented environments, but their reliance on external inputs makes them highly vuln

A GHOST in Long-Horizon Agents: Governance Hazard from Overlooked Safety Constraints across Turns

arXiv:2610.02664v1 Announce Type: new Abstract: Long-horizon agents are now playing an increasingly significant role in assisting humans with complex problem-solving. However, it is exactly their exte

Dynamic Expert Pruning for Multi-Agent Systems

arXiv:2610.02951v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures scale language models efficiently by activating only a few experts per token, but the saving is confined to com

agentsarxiv-cs-ai

KV^2: A Self-Refining KV Cache

arXiv:2610.03198v1 Announce Type: new Abstract: The memory footprint of the key-value (KV) cache constrains the practical use of long-context models, and it dominates cost when one prefilled context m

Prompted to Discriminate: Generalizing Malicious-Input Probes in the Wild

arXiv:2610.02413v1 Announce Type: new Abstract: LLM agents increasingly rely on activation probes as runtime monitors for prompt injection, jailbreaks, and unsafe requests, reading the model's own hid

Speakrail - a low-latency fully-local voice assistant that runs on a single RTX 4090

model-releasesr-localllama

tl;dr: I created a fully-local open-source full-duplex voice agent that rivals GPT-Live on some benchmarks. It uses Voxtral Realtime with a turn-taking head, a microturn-finetuned Gemma 4 12B and Bree