Search: “llm”

Follow the Norm: Accounting for Fine-Tuning and Prompt Effects on Model Rationales

arXiv:2608.13250v1 Announce Type: cross Abstract: Normative datasets are often used to train and align AI systems, but the norms they contain can function as action-guiding patterns rather than neutra

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Safety

I-SDPO: Instance-Level Adaptive Self-Distillation Policy Optimization

DGX agent

arXiv:2608.12957v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) learns from reward differences within a rollout group, but receives no useful relative signal when every sam

safetyarxiv-cs-cl

14 Aug 2026

Identification of Probabilities of Causation: from Recursive to Closed-Form Bounds

arXiv:2505.15274v4 Announce Type: replace Abstract: Probabilities of causation (PoCs) are fundamental quantities for counterfactual analysis and personalized decision making. However, existing analyti

researcharxiv-cs-ai

Interaction Readiness: A Framework for Building and Evaluating AI Agents in Human Roles

arXiv:2608.12358v1 Announce Type: cross Abstract: Product and engineering teams building role-bearing AI agents face an evaluation gap: an agent can produce accurate, safe, and fluent content while st

Is this Citation on Point?

model-releasesarxiv-cs-cl

arXiv:2608.12571v1 Announce Type: cross Abstract: In 2023, a New York judge sanctioned two attorneys in Mata v. Avianca for filing a brief with hallucinated citations generated by ChatGPT. Such failur

Tutorials

Jointly Predicting Courses and Grades Using a Transformer-Based Model

arXiv:2608.13409v1 Announce Type: new Abstract: Existing predictive models in learning analytics often treat student academic history as a simple sequence, overlooking the concurrent nature of courses

tutorialsarxiv-cs-ai

Large Language Models Can Follow Instructions, But Not Many at Once: Phase Transitions in Compositional Constraint Satisfaction

arXiv:2608.12426v1 Announce Type: new Abstract: Large language models are increasingly deployed in settings that require simultaneous adherence to multiple explicit constraints - reasoning structure,

Large Language Models Persuade Without Planning Theory of Mind

arXiv:2602.17045v2 Announce Type: replace Abstract: A growing body of work attempts to evaluate the theory of mind (ToM) abilities of humans and large language models (LLMs) using static, non-interact

safetyarxiv-cs-cl

Latent On-Policy Self-Distillation

arXiv:2608.13040v1 Announce Type: cross Abstract: Enabling agents to learn from experience and internalize it into their policy has become a central problem in self-evolving AI. On-policy self-distill

safetyarxiv-cs-cl

LittleLearner: Language Models Under Pedagogically Controlled Knowledge Exposure

arXiv:2608.13545v1 Announce Type: cross Abstract: Modern language models are trained on heterogeneous web-scale text corpora. Consequently, studying knowledge and skill acquisition is difficult, as pr

MASCOT: Model-Aware Submodular Coverage for Composite-Attribute Text-to-Image Retrieval

arXiv:2608.12532v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are highly effective in retrieving semantically relevant images. However, in practice, relevance alone is often insuffic

researcharxiv-cs-cv

MindMemOS: A Portable and Self-Evolving Memory Operating Layer for AI Agents

arXiv:2608.12428v1 Announce Type: new Abstract: Memory is a core component of AI agents, enabling them to accumulate experience, maintain personalization, and adapt over long-term interactions. Howeve

Moose: Latent concept learning with reasoning-shortcut awareness in EL^{++}

arXiv:2608.12961v1 Announce Type: new Abstract: The OWL 2 EL profile is used in some of the largest production ontologies, including the Gene Ontology and SNOMED CT. Existing neuro-symbolic (NeSy) lea

applicationsarxiv-cs-ai

On Measuring Semantic Preservation in Legal Ontology Learning

arXiv:2608.12326v1 Announce Type: new Abstract: Ontology learning transforms unstructured text into structured representations for automated reasoning. Yet structuring information risks losing it, and

applicationsarxiv-cs-cl

Operationalizing Cyber Threat Intelligence with GraphRAG

arXiv:2608.13050v1 Announce Type: cross Abstract: When a security researcher publishes a report on a cyberattack, detection engineers are supposed to turn it into working detection rules. In practice,

applicationsarxiv-cs-ai

Position: Reasoning is a Learnable Rule-Based Process

arXiv:2608.12325v1 Announce Type: new Abstract: Autonomous reasoning is among the most scientifically and economically motivating topics in AI today. Historically the purview of symbolic AI, recent ad

RAIL: An Automatic Classifier of the Artificial Intelligence Readiness Level

arXiv:2608.13428v1 Announce Type: new Abstract: Assessing the maturity of artificial intelligence technologies is essential for investment decisions, project management, and policy monitoring, yet the

Static analysis-guided agentic AI translation enables Rust as a full stack bioinformatics language

arXiv:2608.13029v1 Announce Type: cross Abstract: The field of bioinformatics struggles with legacy code - old code that is commonly used but may no longer have a maintainer, or may be written in an n

Synthetic Persona Pretraining: Alignment from Token Zero

arXiv:2608.13482v1 Announce Type: cross Abstract: As language-model-based AI is increasingly deployed in autonomous settings, aligning its goals and values with those of humans becomes critical. Today

The Objective Is the Bottleneck: Latent World Models Encode What Their Planners Cannot Use

arXiv:2608.12959v1 Announce Type: cross Abstract: Latent world models are judged by how well they predict, so when planning fails at long horizons the natural reading is that the predictor degrades. O

Thought-Aware KV Cache Compaction for Reasoning via Adaptive Attention Matching

arXiv:2608.12331v1 Announce Type: cross Abstract: Reasoning language models generate lengthy chain-of-thought (CoT) sequences whose key-value (KV) cache grows linearly and becomes a memory bottleneck

researcharxiv-cs-ai

TopoIntent: Compiling Security Intent into Executable, Compliance-Checked Network Topologies

arXiv:2608.13389v1 Announce Type: new Abstract: Enterprise security topology design requires translating business intent, regulatory requirements, and risk assumptions into zones, boundary devices, in

Towards Physics-Faithful Generation of Scientific Diagrams

arXiv:2608.13112v1 Announce Type: new Abstract: Text-to-image generation has reached photorealistic quality, yet state-of-the-art systems remain unreliable at producing scientific diagrams, whose valu

applicationsarxiv-cs-cv

Training AI Scientists to Replicate Research

arXiv:2608.13331v1 Announce Type: cross Abstract: The replicability of papers is a cornerstone of scientific knowledge, ensuring the reliability of existing results and providing a base for further ex

Unlearning at Scale: State-Exact Trace-Preserving Deletion in Billion-Parameter Language Models

arXiv:2508.12220v2 Announce Type: replace-cross Abstract: Can a prospectively instrumented training continuation reproduce a deletion counterfactual exactly after selected examples leave its replay da

When AI Is Your Pastor: A Benchmark for Theological Triage and Pastoral Guidance in Large Language Models

arXiv:2608.12324v1 Announce Type: cross Abstract: People increasingly ask large language models (LLMs) for counsel on questions of faith, doctrine, and pastoral care. These questions are not ordinary

When Can You Trust Offline Evaluation of Equal-Cost Top-k Allocation? A Controlled, Reproducible Benchmark and Practitioner's Guide

model-releasesarxiv-cs-lg

arXiv:2608.12489v1 Announce Type: new Abstract: Organizations decide whom to treat under a budget and want to know what a targeting rule would have earned before deploying it. Off-policy evaluation pr

When Large Language Models are More PersuasiveThan Incentivized Humans, and Why

model-releasesarxiv-cs-cl

arXiv:2505.09662v4 Announce Type: replace Abstract: Large Language Models (LLMs) have been shown to be highly persuasive, but when and why they outperform humans is still an open question. We compare

Where Should Optimizer State Live? Tiered State Allocation for Memory-Efficient Mixture-of-Experts Training

arXiv:2607.19058v2 Announce Type: replace-cross Abstract: Optimizer state is the largest single line item in the memory budget of mixture-of-experts (MoE) training. On a 6.78B-parameter MoE language m

A consequence of how frontier models are trained is motivated reasoning, a phenomenon well studied in humans and discussed in this podcast f…

applicationsyoshua-bengio--x

A consequence of how frontier models are trained is motivated reasoning, a phenomenon well studied in humans and discussed in this podcast from @PalisadeAI. Our thoughts, beliefs and reasonings tend t

Distribird: Literature-Informed Prior Distribution Design for Bayesian Model Calibration

arXiv:2608.11210v1 Announce Type: new Abstract: Bayesian calibration of process-based models requires a prior distribution for each model parameter. Despite decades of methodological work, researchers

Gloss-Free Representation Learning for Cross-Dataset Sign Spotting

model-releasesarxiv-cs-cl

arXiv:2608.11332v1 Announce Type: new Abstract: Sign-language research for resource-constrained languages is often limited by the cost of dense linguistic labels such as glosses, temporal boundaries,

Grounding Large Language Models as Generalizable Policies in Network Control

arXiv:2512.11839v2 Announce Type: replace Abstract: Designing generalizable control policies that operate reliably under changing conditions is essential for robust network services in modern digital

safetyarxiv-cs-lg

Hybrid-Policy Self-Editing for Composable Unstructured Knowledge Editing

arXiv:2608.11660v1 Announce Type: cross Abstract: Large language models (LLMs) achieve remarkable performance across natural language tasks, yet they are trained on static corpora and their knowledge

One Frozen Simulator Is Not Enough: Simulator Collapse in Multi-Agent RL

arXiv:2608.12253v1 Announce Type: cross Abstract: Multi-agent reinforcement learning for human-AI interaction typically relies on a single large language model to simulate user behavior. We show that

TradingMoE: Routing the Right Experts in Evolving Markets

arXiv:2608.11785v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential for financial analysis and trading, but direct trading remains challenging because the predicti

researcharxiv-cs-lg

Trained a 1.5B to write shell commands so I'd stop googling tar flags. Runs on a laptop CPU in ~1 sec.

model-releasesr-localllama

I've been googling 'tar extract gz' for about ten years. and I finally did something about it. It started out as a research project and I ended up with a Fine-tuned Qwen2.5-Coder-1.5B on 125k natural-

When Unlearning Is Free: Leveraging Low Influence Points to Reduce Computational Costs

researchapple-ml-research

As concerns around data privacy in machine learning grow, the ability to unlearn, or remove, specific data points from trained models becomes increasingly important. While state of the art unlearning

A Cost-Efficient Routing Pipeline for Multilingual Short-Text Classification Using Small Language Models

arXiv:2608.10939v1 Announce Type: cross Abstract: Multilingual short-text classification supports operational systems such as content moderation, customer support routing, and intent recognition, yet

applicationsarxiv-cs-ai

Actions Speak Louder than Words: Measuring Cross-Lingual Policy Retention in Tool-Using Agents

arXiv:2608.11110v1 Announce Type: new Abstract: When a tool-using agent is given the same task in a different language, does it still take the same steps? Multilingual evaluation rarely asks: it compa

safetyarxiv-cs-cl

Automating and Scaling Behavioral Scientific Research on AI Agents

arXiv:2608.10030v1 Announce Type: new Abstract: As AI agents are increasingly deployed in complex environments, understanding their behaviors becomes critical. Yet behavioral scientific research on AI

Bandwidth-Efficient Multi-Agent Communication through Information Bottleneck and Vector Quantization

arXiv:2602.02035v2 Announce Type: replace-cross Abstract: Multi-agent reinforcement learning systems deployed in real-world robotics applications face severe communication constraints that significant

Behavioral Inference at Scale: The Fundamental Asymmetry Between Motivations and Belief Systems

model-releasesarxiv-cs-lg

arXiv:2509.05624v3 Announce Type: replace-cross Abstract: How much information about an agent's underlying values can be recovered from its observable behavior? This question matters for any approach

CARE: Confidence-Aware Reasoning for Reliable Medical VQA

arXiv:2608.10964v1 Announce Type: cross Abstract: Reinforcement Fine-Tuning (RFT) has enabled medical Multimodal Large Language Models (MLLMs) to produce Chain-of-Thought (CoT) reasoning for visual qu

Local Ai

Certify or Refuse: A Cross-Model Map for Selective Risk Control with Coverage Floors under Covariate Shift

arXiv:2608.10893v1 Announce Type: new Abstract: Certified selective predictors attain whatever coverage they attain; operators impose an automation floor: answer at least a eta-fraction of shifted tar

local-aiarxiv-cs-cl

CHORUS: Complementary Experts for High-Coverage Testbench Stimulus Generation

arXiv:2608.10090v1 Announce Type: new Abstract: Large language models (LLMs) have advanced code generation, where executable feedback provides a more reliable learning signal than textual imitation al

Co-Evolution in Agentic Systems: Toward Self-Directed Evolution Beyond Human Design

arXiv:2608.10299v1 Announce Type: new Abstract: Agentic systems are increasingly expected to improve after deployment, yet single-entity self-evolution is often bounded by a static learning context, s

agentsarxiv-cs-cl

Conflict or Strategy? Asymmetric Role Framing of La France insoumise and Rassemblement National in French News Headlines, 2022-2025

arXiv:2608.09936v1 Announce Type: new Abstract: Do French news headlines frame left- and right-populist challengers as symmetric ``extremes,'' or as fundamentally different political adversaries? We e

researcharxiv-cs-cl