Search: “llm”

MemCodex: Self-Programming Hierarchical Memory for Language Agents

arXiv:2609.39765v1 Announce Type: new Abstract: Agent memory faces heterogeneous access needs: a single-hop question may require one piece of evidence, whereas a multi-hop question must combine eviden

agentsarxiv-cs-cl

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MILO: Automated Harness Discovery via Orchestrated Multi-Agent Evolution

DGX agent

arXiv:2609.38349v1 Announce Type: cross Abstract: Modern agentic systems combine an AI model with a harness that controls execution and environmental interactions. Harness design strongly affects long

agentsarxiv-cs-ai

1 Oct 2026

MiniRep: Robust Reputation-Based Aggregation for Multi-Agent Debate

arXiv:2609.39297v1 Announce Type: new Abstract: Autonomous agents powered by large language models (LLMs) are rapidly evolving into an open agentic ecosystem. To support trustworthy collaboration, ind

More Choices, Fewer Decisions: Ordinal-Scale Bias in JEV-like Direct-Decision Models

arXiv:2609.38827v1 Announce Type: new Abstract: Direct-decision models turn text into low-latency structured labels and scores, making them attractive for classification and automatic evaluation. Yet

NinaXander: Feasibility and Limits of Composing Frozen Language Models Across Architecture Families via a Shared Latent Space

arXiv:2609.38261v1 Announce Type: cross Abstract: In this paper we propose NinaXander, a series of composed language models obtained by connecting layers of frozen language models from different archi

NMIXX: Domain-Adapted Neural Embeddings for Cross-Lingual eXploration of Finance

arXiv:2507.09601v3 Announce Type: replace-cross Abstract: Financial text embeddings must distinguish changes in event status, perspective, and obligations even when passages share similar wording. NMI

OverdoseMoE: A Multi-Expert Framework for Opioid Overdose Risk Prediction

model-releasesarxiv-cs-cl

arXiv:2609.40108v1 Announce Type: new Abstract: Opioid overdose remains a major clinical and public health burden, highlighting the need for scalable approaches to identify patients at high risk. Here

Positive Ratings, Hidden Concerns: Employee Voice Disclosure in AI-Mediated Organizational Listening

arXiv:2609.38788v1 Announce Type: new Abstract: Organizations started listening to employees through conversational AI agents alongside structured surveys. Little is known about what these channels ch

Privacy in Personalized AI Is a System Property, Not Just a Model Property

arXiv:2609.38289v1 Announce Type: cross Abstract: In personalized AI applications, such as conversational assistants and recommender systems, users interact not with models in isolation but with broad

researcharxiv-cs-lg

Ranking-Aware Prompt Optimization for Multimodal Clinical Diagnosis

arXiv:2609.40361v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are rapidly advancing clinical diagnosis, yet their adaptation pipelines remain anchored to accuracy-based ob

Revisiting scaling laws for reward optimization

arXiv:2609.38526v1 Announce Type: cross Abstract: Scaling laws for optimization against reward models in AI alignment have pinned down how performance depends on optimization effort---measured by a KL

RSIGame: Autonomous Agentic Game Development with Recursive Self-improvement

model-releasesarxiv-cs-cl

arXiv:2609.39045v1 Announce Type: new Abstract: Recent advances in large language models have made automatic game generation increasingly feasible, yet reliably improving generated games beyond a play

Scoring Higher, Answering Worse: Mitigating Reward Hacking in Rubric-Based RL via Protocol-Level Rubrics

arXiv:2609.38847v1 Announce Type: cross Abstract: Rubric-based reinforcement learning (Rubric-RL) trains language models where no verifier exists. A judge checks each criterion of a rubric, and the ve

Self-Evolving Algorithm-Design Agents: Escaping In-Context Evolutionary Stagnation via Population-Curated Policy Optimization

arXiv:2609.38757v1 Announce Type: new Abstract: Large language models are increasingly participating in complex real-world tasks in the form of algorithm-design agents, designing and refining algorith

Hardware

Shared Phase and Retention Control for Efficient Adaptive Spectral Recurrence

arXiv:2609.39082v1 Announce Type: new Abstract: As new evidence arrives, a sequence model must update what it remembers and how memory influences predictions. While Transformers incur computation and

hardwarearxiv-cs-lg

Staying on Task: Testing the Foundations of Long-Horizon Agent Reliability

arXiv:2609.38712v1 Announce Type: new Abstract: Long-horizon agentic workflows require models to sustain repeated state-dependent actions all while the context grows, sub-task complexity changes, and

Superficial Reflection or Genuine Thought? A Fine-Grained Cognitive Analysis of Large Reasoning Models

arXiv:2512.00729v2 Announce Type: replace Abstract: Motivated by the observed human-like behaviours in Large Reasoning Models (LRMs), this paper introduces a comprehensive taxonomy to characterise ato

Survival is the Only Reward: Sustainable Self-Training Through Environment-Mediated Selection

arXiv:2601.12310v2 Announce Type: replace Abstract: Self-training systems often degenerate due to the lack of an external criterion for judging data quality, leading to reward hacking and semantic dri

Synthetic Pre-pretraining Survives Scale, but Not as a Grammatical Prior

arXiv:2609.39827v1 Announce Type: cross Abstract: Pre-pretraining (PPT) on synthetic non-natural language data improves token efficiency during language model pre-training (PT). Prior work attributes

Tutorials

TACIT: Optimization Models that Learn from Their Mistakes

arXiv:2609.38434v1 Announce Type: cross Abstract: Real-world optimization problems are difficult to model accurately because many objectives and constraints reside in domain experts' tacit knowledge,

tutorialsarxiv-cs-ai

Targeted Retrieval, Compact Representations: How CoT Reasoning Improves Long-Context Counting

arXiv:2609.38958v1 Announce Type: new Abstract: Large language models (LLMs) have been rapidly improving in long-context tasks, powered by Chain-of-Thought (CoT) reasoning. However, the internal mecha

The Row Normalization Puzzle in Muon

arXiv:2609.39114v1 Announce Type: new Abstract: This paper examines how row-wise renormalization affects Muon, focusing on the gap between NorMuon's worst-case guarantees and its practical performance

researcharxiv-cs-lg

The System Prompt Illusion: How Instruction Preambles Modify Computation in Language Models

arXiv:2609.38205v1 Announce Type: new Abstract: System prompts are the primary lever practitioners use to control language model behavior, yet what they actually do to the computation inside the trans

safetyarxiv-cs-cl

Thinking Outside the Box: Can Language Models Rely on External Guidance Selectively?

arXiv:2609.39578v1 Announce Type: new Abstract: Agent harnesses often improve language models with human-designed workflows, but as models grow more capable, unreliable guidance can increasingly const

agentsarxiv-cs-cl

Trust Is Not a Score: Runtime Assurance Contracts for High-Risk AI Agents

arXiv:2609.39717v1 Announce Type: new Abstract: Benchmarks, audits, and agent protocols describe performance, permissions, and repair, but not how observed evidence should change an agent's authority

TutlAit v1: a crowdsourced Moroccan Tamazight speech dataset with Arabic transcriptions and regional accent labels

arXiv:2609.38219v1 Announce Type: new Abstract: Tamazight (Amazigh) is, together with Arabic, one of the two official languages of Morocco, yet it remains severely under-resourced for speech technolog

Uncertainty-Normalized Margins for Direct Preference Optimization

arXiv:2609.38647v1 Announce Type: new Abstract: Direct preference optimization (DPO) models binary preferences through a Bradley-Terry model with a common noise scale, without explicitly accounting fo

Uncovering Uncontrolled Repetition through Residual Stream Dynamics

arXiv:2609.38802v1 Announce Type: cross Abstract: Uncontrolled repetition can prolong autoregressive generation in large language models (LLMs) and enable resource consumption attacks. Prior analyses

When Does a Spoken Agent Have Enough Evidence to Act? The PACT-SLM Contract Test

arXiv:2609.38232v1 Announce Type: cross Abstract: Streaming spoken agents may take an external action before the available speech supports it, yet final-turn scores do not reveal whether each observed

agentsarxiv-cs-cl

Where the Evidence Lives: Auditing AI Companions' Self-Descriptions

arXiv:2609.38753v1 Announce Type: cross Abstract: Companion agents describe themselves: they remember, they understand their users, the relationship has changed them. We argue that such accounts, and

Who Owns That? Evaluating Ownership Intuitions in Large Language Models

arXiv:2609.39483v1 Announce Type: new Abstract: Ownership establishes rights over the use, control, and transfer of objects. Understanding these relations is essential for AI systems to interact appro

Agentic AI for Scalable and Robust Optical Systems Control

arXiv:2602.20144v2 Announce Type: replace-cross Abstract: We present AgentOptics, an agentic AI framework for high-fidelity, autonomous optical system control built on the Model Context Protocol (MCP)

Hardware

AI as a Compiler: Compiling Triton kernels without the Triton compiler

arXiv:2609.36800v1 Announce Type: new Abstract: Compiler backends are expensive to build and maintain as programming models, workloads, and accelerators evolve. We investigate whether large language m

hardwarearxiv-cs-ai

AnyAct: Universal Action for Self-Evolving Agents

arXiv:2609.37025v1 Announce Type: new Abstract: As large language models (LLMs) advance, AI agents are increasingly deployed in open-world environments to tackle complex sequential tasks (e.g., docume

Automated Evaluation of Multi-Turn Dialogues in In-Car Conversational Assistants

arXiv:2609.35812v1 Announce Type: new Abstract: In-car conversational assistants (ICAs) are increasingly integrated into vehicles to support route planning, vehicle control, and information access. En

safetyarxiv-cs-cl

CALIBUDGET: Calibration-Guided Source Allocation for Fixed-Budget Mixed-Reasoning Adaptation

arXiv:2609.36721v1 Announce Type: new Abstract: Fixed-budget adaptation from heterogeneous data sources requires deciding not only how much data to use, but how much exposure each source receives. Siz

Can We Still Trust Disaster Social Sensing? Empirical Evidence on Detecting AI-Generated Social Media Posts

arXiv:2609.35821v1 Announce Type: new Abstract: Disaster social sensing converts public social-media posts into evidence for situational awareness and humanitarian needs, but generative artificial int

Cognitive Expert Language Models Better Align with the Corresponding Brain Systems

arXiv:2609.36239v1 Announce Type: new Abstract: Large language models (LLMs) can predict human brain activity across a variety of brain regions during natural language comprehension. Typically, howeve

safetyarxiv-cs-cl

Evaluating the Effects of Prompt Perturbation on Bias and Hallucination in Large Language Models

arXiv:2609.35804v1 Announce Type: cross Abstract: Large language models (LLMs) have shown remarkable capabilities in various natural language processing tasks, leading to their widespread deployment a

FloCA: Towards Faithful and Logically Consistent Flowchart Reasoning

arXiv:2602.14035v2 Announce Type: replace Abstract: Flowchart-oriented dialogue (FOD) systems aim to guide users through multi-turn decision-making or operational procedures by following a domain-spec

From Learner Behavior to Reusable Skills for Effective and Efficient Learner Simulation

arXiv:2609.37157v1 Announce Type: new Abstract: Learner simulation aims to reproduce how a particular learner behaves on new tasks. Although Large Language Models (LLMs) can generate increasingly fine

Infrared Subtraction with Artificial Intelligence

arXiv:2609.36007v1 Announce Type: cross Abstract: We present AI-developed local infrared subtraction, building on projection to Born and EFT matching. The framework separates an integrable radiation t

Is manual software optimization a thing of the past?

arXiv:2609.37849v1 Announce Type: cross Abstract: Scientific software is increasingly required to process larger datasets while maintaining acceptable execution times. Software optimization traditiona

Learning from the Gap Between Pass@K and Pass@1

arXiv:2609.35793v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly trained with reinforcement learning from verifiable rewards (RLVR). An exact verifier can also support tes

Look What You Made Us Cluster: Hate Narrative Extraction from Reddit Discourse

arXiv:2609.37408v1 Announce Type: new Abstract: Narrative extraction allows us to identify online hate narratives, supporting the construction of rigorous detection systems. Existing computational app

Lost in Translation: Measuring the Effect of Non-Native English on End User Performance of Large Language Models

arXiv:2609.36214v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used by people whose first language is not English, yet these users have been shown to receive systematica

Making Analog Training Scale: Co-Designing Mapping, Optimizer, and Converters

arXiv:2609.36584v1 Announce Type: new Abstract: Analog in-memory computing (AIMC) offers an alternative for model training by executing matrix operations directly where weights are stored. However, sc

Tutorials

MemEvo: Automatic Discovery of Streaming Video Memory Mechanisms

arXiv:2609.36581v1 Announce Type: new Abstract: Query-agnostic streaming video understanding requires vision-language models to continuously compress an indefinitely growing visual stream into a bound

tutorialsarxiv-cs-ai