#llm-agents

11 articles

01 ·Large Language Models·★ MEMBER·PAPER·9 min read Paper Explained: Just-in-Time Memory — Curate Agent Memory When You Read It, Not When You Write It A from-scratch walkthrough of JitMem, which stores raw agent trajectories and only summarizes them once the next task is known. Why that makes the curator easy to train, and exactly how far the reported numbers go. 02 ·★ MEMBER·PAPER·11 min read Paper Walkthrough: SceneMosaic — Physically Valid 3D Rooms from One Image, in Minutes, in Many Variations How to turn a single picture of a room into a simulation-ready 3D scene with zero collisions, in minutes rather than hours, and with several coherent furniture rearrangements thrown in. A walkthrough of how SceneMosaic stitches the speed of image-to-3D models to the precision of VLM agents, with the equations and interactive figures. 03 ·Agents·★ MEMBER·PAPER·12 min read HarnessDev explained (arXiv:2609.01437) — agent harness benchmark Identical weights, different harness: GPT-5 solves 35.2% of Terminal-Bench 2.1 inside Terminus 2 and 49.6% inside Codex CLI. HarnessDev asks whether a model can build its own harness from a useless seed — and what survives held-out tasks. 04 ·Agents·★ MEMBER·PAPER·14 min read Paper Walkthrough: Aspire — Can Models Self-Evolve from Vague Goals? Hand an agent nothing but "get better at mathematical reasoning" and let it decide what to learn and how to check itself. Aspire measures the result on 520 hidden, expert-written items — and finds that agents close the training loop far more reliably than the capability loop. 05 ·Agents·★ MEMBER·PAPER·12 min read Paper Explained: Repo-To-Skill — Distilling GitHub Repositories Into Skills an AI Can Use A third layer that is neither the model nor the harness: operational knowledge, distilled from 1,000 GitHub repositories into 5,353 verified skills. What DisCo and the AREX-Skill Library actually do, what 31.11%→72.89% on MLE-bench is made of, and where the skills failed. 06 ·Agents·★ MEMBER·PAPER·11 min read Paper Walkthrough: AutoSaddler — Growing a Harness That Doesn't Break, from Agent Failure Logs A ground-up walkthrough of AutoSaddler, which automatically optimizes the harness around an LLM agent — prompts, tools, and middleware — by repeatedly diagnosing failure traces and generating structured patches. It beat the base harnesses on GAIA2, SWE-Bench Pro, and Terminal-Bench 2.0 by 9.0, 9.6, and 10.0 points. 07 ·Inference & Serving·★ MEMBER·PAPER·11 min read Paper walkthrough: CyberFactory — turning wild CVEs into runnable training problems An open-source pipeline that converts real CVEs into executable, verifiable tasks and uses a reusable vulnerability-analysis skill to synthesize teacher trajectories the student then internalizes. 58.1% Pass@1 on CyberGym. 08 ·Agents·★ MEMBER·PAPER·9 min read Paper Explainer: Why Agent Skills Work — and Where They Break Handing an agent a written procedure improves its scores. This paper dissects why, using 8,135 trial records and 528 matched trajectory triples. Skills work as procedural anchors rather than knowledge injection — and they break once the skill library grows. 09 ·Paper Deep-Dives·★ MEMBER·PAPER·11 min read Paper Walkthrough: WorldClaw — Agents That Build Walkable, Editable 3D Open Worlds from a Single Sentence Type 'a tropical pirate stronghold' and get a large-scale 3D world you can walk through — with every building still an editable, standalone asset. A walkthrough of Tencent Hunyuan's WorldClaw, from its plan → terrain → regional-objects pipeline to the render-and-inspect loops where agents check their own work. 10 ·Inference & Serving·FREE·PAPER·8 min read Paper Walkthrough: CodeNib — A Multi-View Data System That Serves Repository Context to Coding Agents Coding agents grep their way through the same repository over and over. CodeNib (UC San Diego et al.) reframes this as a database problem — lexical, dense, and structural views over an immutable commit — and measures the whole lifecycle, caveats included. 11 ·Paper Deep-Dives·★ MEMBER·PAPER·8 min read Paper Deep Dive: AskChem — Changing the Unit of Search from Papers to Provenance-Carrying Claims An NYU-led systems paper that swaps the retrieval unit of chemistry literature search from papers to claims grounded by a DOI and a verbatim quote. We walk through the 2.4M-claim index, evidence graph, and hybrid search design, the evaluation that took DOI resolution from 88.3% to 100%, and the paper's honest limitations.