Wireless signals with position-related labels are pivotal for both performance evaluation …
Data-sovereignty regulations increasingly require public institutions to deploy open-sourc…
LLM decision components that can operate within agent workflows often produce action-relev…
Large language models (LLMs) are increasingly evaluated on molecular property benchmarks, …
Computer-use agents have advanced on benchmarks like OSWorld and AndroidWorld, but still a…
The integration of GenAI tools into higher education assessment raises important questions…
Model upgrades are routine; memory migrations are not. An agent can keep the same memory s…
A transformer language model assigns a single, context-independent vector to a word type a…
Quantum circuits are central to implementing quantum algorithms on quantum devices, where …
Building automation systems generate rich sensor data yet remain insight-poor because hete…
On-policy distillation (OPD) provides dense, per-token supervision for language model post…
Automated reference-based evaluation methods play a critical role in assessing natural lan…
Recent years have witnessed great advances in the reasoning ability of Large Language Mode…
Production multi-agent systems replace agents constantly, on the assumption that an agent …
Layer dropout (a.k.a. stochastic depth) has been shown to enable faster training, higher a…
Artificial intelligence (AI) is transforming not only what information systems researchers…
Large language model agents increasingly rely on execution traces to master complex intera…
Commonsense reasoning in computer vision encompasses integrating visual data and contextua…
We present a unified reinforcement-learning (RL) framework that discovers compact parametr…
Human knowledge is inherently structured and interdependent: mastery of a concept requires…
Language-model judges now gate training data, score generations, and drive leaderboards. T…
Explaining why a specific outcome occurred, and which inputs deserve the blame or credit, …
On-policy distillation (OPD) combines student-generated rollouts with dense token-level su…
Multi-agent AI science ecosystems rely on agents possessing tools that allow them to commu…
Research and news coverage of language-model deception increasingly attributes human-like …
As terminal-based code agents become prevalent, agent trajectories have accumulated at sca…
AI agents are trained on population-scale data to encode broad capabilities spanning those…
AI agents are increasingly being developed and deployed across organizations using heterog…
Scaling interactive and verifiable environments is critical for training terminal agents. …
Large language models are increasingly used to support organizational decisions, yet users…
Hybrid LLMs pair softmax attention with linear-attention layers such as Gated DeltaNet (GD…
Reinforcement Learning from Verifiable Rewards works well when a task has a programmatic c…
Group Relative Policy Optimization (GRPO) is widely studied for reinforcement learning wit…
IRWOZ has improved industrial human-robot interaction (HRI) dialogue systems through domai…
Procedural instruction following is a basic requirement for controllable language-model sy…
Evaluating large language models (LLMs) in safety-critical, physics-governed environments …
For trained operators, gauge reading requires little specialized knowledge, low cognitive …
Early screening of chronic kidney disease (CKD) is critical for timely intervention, yet m…
We present an alternative characterization of the occupancy measure of reinforcement learn…
An image editor may satisfy every regional plausibility constraint separately even when no…
Recent web agents use world models for test-time action selection by sampling candidate ac…
Researchers increasingly use artificial intelligence to construct measures of social, orga…
Root cause analysis (RCA) is a critical task in telecom network operations, but diagnosing…
The performance of LLM-based agents is jointly shaped by the base model and the harness us…
On-premise assistants can give factory workers conversational access to machine documentat…
Multi-agent LLM systems commonly use an orchestrator to decompose a task for a team of wor…
Autonomous agents are beginning to carry out machine-learning (ML) research end to end. Th…
Does the door-in-the-face technique work on language models? In humans, a large request th…
Aggregating noisy, conflicting textual hypotheses into a reliable consensus is a fundament…
Generative AI is changing how cultural artifacts are created and circulated, and with it o…
We present CivBench, an open-source benchmark for evaluating language model agents in long…
Large Language Models (LLMs) have recently demonstrated strong capabilities in automated t…
Argumentation frameworks are useful tools for representing and reasoning with information …
With the proliferation of LLM agents, the ability to understand and diagnose failures in a…
Effective in-context learning (ICL) for complex reasoning relies on selecting the right de…
A core obstacle to alignment evaluation is evaluation awareness: capable models can tell w…
The rapid proliferation of large language models (LLMs) and the growing diversity of their…
Model merging provides an efficient paradigm for constructing multi-task large language mo…
Adapting the communication topology of an LLM multi-agent system to each query improves bo…
Deep research agents augment large language models with external tools to answer complex, …