#llm-agents
10 articles
01
·Agents·★ MEMBER·PAPER·12 min read
Paper Walkthrough: HarnessDev — Can an LLM Build and Maintain the System It Runs Inside?
A ground-up walkthrough of HarnessDev, a benchmark that asks whether an LLM can build an agent harness from a deliberately useless seed and then improve it from execution feedback. Models can build one; the gains rarely survive contact with held-out tasks or a different runtime model.
02
·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.
03
·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.
04
·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.
05
·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.
06
·Agents·★ MEMBER·PAPER·9 min read
Paper Explained: EnvHarness — Reshaping an Agent's Training World Without Rebuilding It
The environments LLM agents train in are hand-built and frozen: blind to the agent's weaknesses and left behind as it improves. EnvHarness wraps a static environment in plug-in components that reshape its behavior without touching the underlying logic — and keep the original verifier intact.
07
·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.
08
·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.
09
·Inference & Serving·★ MEMBER·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.
10
·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.