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Earlier sources: AI coding agents
AI coding agents use models and development tools to inspect, modify, and test software. Follow coding workflows, repository tooling, evaluations, and evidence about where these systems succeed or fail.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗87 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- arXiv · Published · First archived
WitnessGym: Benchmarking Coding Agents on the Construction of Bug Witnesses ↗
Bug validation asks a coding agent to produce an executable witness for a reported bug. The witness combines a concrete input with a testing harness and exposes faulty behavior during execution. Such evidence makes audit findings actionable, yet benchmark evaluation is difficult when cases reuse public historical bugs and witnesses or require manual construction. We present WitnessGym, an automated framework for constructing bug-validation benchmarks through bug injection. It injects bugs into test-reached paths of real projects, rebuilds each project, and retains cases exposed by a constructi…
- arXiv · Published · First archived
Tracekit: Tamper-Evident Intent-Reasoning-Action Auditing for Autonomous Coding Agents ↗
Autonomous coding agents read untrusted files, run shell commands and spawn sub-agents with little supervision, yet their record is usually an editable log. We present Tracekit, an open-source, dependency-free system that captures three channels for every agent session: what the human asked (intent), what the model said of its reasoning (self-report), and what it actually executed (actions). These are written to a hash-chained, externally anchorable ledger and cross-checked. Tracekit hooks into Claude Code's lifecycle events, reconstructs multi-agent hierarchies, gates tool calls with a pre-ex…
- arXiv · Published · First archived
The Compiler May Read It, the Agent May Not: Keeping Part of a Research Code Away from a Coding Agent ↗
The compiler must read modules a physics-based solver cannot build without; the coding agent must not read that intellectual property. The harness does not ship that rule. We classified fifteen read routes against a container, permission rules and a sandbox. None of the three can tell which program is reading.
- arXiv · Published · First archived
Do Coding Agents Reuse Existing Code or Reinvent the Wheel? ↗
Coding agents are increasingly deployed for iterative development on real repositories, yet existing evaluation barely answers a basic question: \emph{do coding agents reuse existing code or reinvent the wheel?} The question matters: every duplicated implementation is a fix applied twice and agents produce code far faster than humans can audit, so redundancy accumulates unsupervised. Thus, we present \textbf{RepoReuse}, a multi-turn benchmark for auditing code reuse in real repositories, where requirements are revealed turn by turn and the workspace accumulates across turns. It is built by a f…
- GitHub · Repository created · First archived
doronp/gitmemory ↗
Git-versioned, contiguity-checked memory for AI agents: verbatim transcripts, byte-offset recall, no LLM
- Hacker News · Posted on Hacker News · First archived
Detecting Compromised AI Coding Agents with Jev and Gryph ↗
Hacker News discussion.
- arXiv · Published · First archived
Opera: A Verbal Critic Framework for Long-horizon Coding Agents ↗
Long-horizon coding agents need timely corrections, yet feedback can be ineffective or even harmful when it misjudges ongoing work or fails to address the underlying problem. Existing critics focus on evaluating trajectories and generating feedback, but rarely track what happens after feedback is delivered. We present Opera, a verbal critic framework that treats each correction as a persistent note, followed until the diagnosed problem is resolved. Opera decides when to review through periodic and event-driven triggers, diagnoses issues with typed operators, audits feedback against visible evi…
- GitHub · Repository created · First archived
Hugelidus/Pointcast ↗
Talk and Alt+click on your web app; Claude Code, Codex, Gemini CLI or Cursor get the exact elements and the lines of code behind them. Local Whisper.
- Hacker News · Posted on Hacker News · First archived
Show HN: swe-mux – A terminal multiplexer for coding agents optimized for mobile ↗
I run a lot of coding agents at once, mostly Claude Code, Codex and pi, across several projects. I got tired of having so many windows open, of agents leaving hung processes behind, of tracking which one needed input, and of having to remote into my desktop or SSH into a single shell to manage them from my phone. Even then, a terminal on a phone couldn't show me the frontend or output I needed to verify the work. I tried Orca and herdr. Both were…
- GitHub · Repository created · First archived
scarletkc/seiso ↗
A Markdown convention and linter for project docs written by AI and read by humans and agents
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about AI coding agents
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.