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19,710 papers
Repository-level coding benchmarks largely assume that tasks and environments remain unchanged throughout execution. In software development, code, dependencies, runtime state, and requirements can change while an agent is still working. We introduce SWE-Shift, a benchmark for evaluating coding agents under such mid-ex...
Users often exhibit changes in interaction behavior before explicitly seeking assistance, enabling systems to anticipate future assistance use. This is especially important in high-stakes environments, where delayed assistance can have operational, financial, or safety consequences. However, conventional Large Language...
Large language models (LLMs) are increasingly used to support decisions under incomplete information, where deciding what to look up and when to stop matters as much as the final answer. However, LLMs fall into an information trap: They pay for checks, i.e., costly pieces of evidence, that cannot change their decision...
Humans reason about the physical world by flexibly combining different forms of inference as a problem unfolds. Achieving this flexibility is a major open challenge in AI: capabilities often remain separated across specialized models, limiting how they can be composed and jointly adapted to a task. We introduce $\Psi$-...
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Post-training quantization (PTQ) is a key technique for efficiently deploying large language models, as it compresses weights into low-bit representations without retraining. Existing PTQ solvers, including GPTQ, LDLQ, and block coordinate descent, commit each weight block to a single codeword before its neighbors are...
Agents and verifiers often rely on the text a tool prints, and that text can misrepresent what the tool actually did, even when the tool's computation is correct. We saw this happen in a production RAG research system used by the Pilsudski Institute in New York, where tool output formatters misled an agent into giving...
PersonalityScienceBench evaluates how agents collaborate with diverse researchers alongside whether they complete the task, and across eighteen modelconfigurations, task performance, fact recovery, and self-repetition reveal different strengths.
This work compares the proposed waveforms with superposition in the time domain and learned spectral linear fusion on 512 independent test images, using a shared fixed UE receiver and a baseline sensing receiver using coherent peak search.
DELA is introduced, a plug-in framework that enables controllable draft refinement with a frozen DLM and provides a modular approach to explicit draft-level control in diffusion language models.
This work develops a general framework that connects data-driven behavioural discovery with analysis and experimental investigation for trajectories of varying length, event order and agent roles, and identifies advances in coding, analysis and experimentation needed to support its wider use.
DriveMind is proposed, an MLLM-based driving framework that keeps several hypotheses about the scene alive at the same time and updates their weights as new frames arrive and improves fine-grained interactive driving abilities such as merging, emergency braking, and give-way.
A corpus-grounded taxonomy of communication failures, built from 3,211 failed multi-round trajectories across medical QA, coding, finance, multi-hop reasoning, ethics, and embodied text tasks, finds that failures are dominated by recovery/correction and consensus/decision problems rather than isolated first-round mista...
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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