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20,463 papers
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.
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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...
HyScale, a long-context training system for learned sparse attention, introduces HyTopK and selection replay, and speeds up GLM-5.1 training on 1M-token sequences by 2.07$\times$ over Megatron-LM on 512 H20 GPUs.
TTME is introduced, a plug-and-play test-time adaptation method that continually updates the external memory by distilling the experience to guide subsequent discovery without updating model weights, and established as an effective and computationally efficient alternative to TTT in the evaluated discovery settings.
This work asks whether model safety is preserved when an LLM is integrated in an AI-for-Science agent harness, and evaluates five agent harnesses on 50 harmful chemistry requests to show that the safety behavior of the base LLM is not durable once a harness is introduced.
The growing population of orbital debris has intensified interest in active debris removal and on-orbit servicing, for which dual-arm space manipulators offer improved grasping stability and load distribution relative to single-arm systems. However, coordinating two manipulators under inter-arm collision avoidance, ori...
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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