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Earlier sources: Artificial Intelligence
Artificial intelligence covers systems that learn from data, generate content, perceive their surroundings, or act toward a goal. Follow model research, agent tooling, evaluation, and the infrastructure used to build and run these systems.
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 ↗5813 classified signals in the fourteen-day window. Recent momentum: steady. 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.
- Hacker News · Posted on Hacker News · First archived
Fakegreen: Catch AI coding agents faking a green build (no LLM) ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Strategies for Pricing an AI Coding Agent Subscription to Ensure Profitability ↗
Hacker News discussion.
- arXiv · Published · First archived
Toward Intelligent Networks via AI-Aware GPU-Native Packet Processing ↗
The integration of inline Artificial Intelligence (AI) models into critical network infrastructure is fundamentally bottlenecked by the high latency and synchronization overhead of CPU-mediated packet processing. While legacy GPU offload and recent CPU-bypass frameworks attempt to bridge this gap, they remain trapped in proprietary ecosystems or still rely on the host CPU and coarse-grained batching to coordinate stateful telemetry and AI pipeline execution. In this paper, we propose AGP, a novel framework that promotes the GPU from a passive accelerator to a primary data-path controller, answ…
- arXiv · Published · First archived
Poster: A Preliminary Study of LLM Distillation Inference ↗
Unauthorized model distillation, in which a model is trained on the outputs of a proprietary large language model (LLM), is a growing threat to model providers. We study distillation inference: determining whether a suspect model was distilled from another model or trained independently. We formulate this problem as a hypothesis test and estimate the behavior expected under each hypothesis by training shadow models: distilled shadow models learn from the teacher's reasoning traces, whereas independent shadow models learn only from reference answers. The auditor measures how closely each model…
- GitHub · Repository created · First archived
Timofey121/OrchestraKit ↗
Codex-first orchestration with bounded agents, adaptive model routing, verified results and a local dashboard.
- Hacker News · Posted on Hacker News · First archived
'Dystopian': Co-op becomes latest firm to put staff under AI surveillance ↗
Hacker News discussion.
- arXiv · Published · First archived
Could LLM Watermark Detection be Public? ↗
Watermarking large language models is popular for tracing chatbot and agentic outputs, yet detectors remain unreleased since exposing them could let attackers do targeted edits with the detector's feedback. However, watermarks are already vulnerable to uninformed tampering attacks. We thus first quantify whether a public detector would be an additional liability in a deployment setting at varying levels of access, from token-level scores to a binary verdict. Second, we introduce a split-key public-private watermarking method that exposes one key through a public detector while keeping the othe…
- Hugging Face · Model / dataset date · First archived
springofwindslabs/function-calling-en-trial — dataset update ↗
# 🛑 Most Function-Calling Failures Are Not Schema Failures. They Are State Failures. The model believes the world is still valid — when reality has already changed. It calls the right tool with stale beliefs, gets `"success"`, and moves on. That is how agents silently corrupt production state. This dataset trains **argument-level correctness under changing state**: multi-turn tool use, error recovery, and explicit verification steps — not just happy-path calls. **Function Calling Dataset (EN)** — Official Open‑Source Evaluation Package (50 Rows Subset) by **springofwindslabs** > **Full produc
- Hugging Face · Model / dataset date · First archived
springofwindslabs/mcp-agent-trajectory-benchmark — dataset update ↗
# 🛑 Stop LLM Agent Collapse Caused by State Corruption **Most agent failures are not tool failures. They are state failures.** The model believes the world is still valid — when reality has already changed. This MCP trajectory dataset trains **belief revision**, **state recovery**, and **autonomous replanning**: ```text believed : 45/50 rooms synced ← agent trusts a stale worker log tool : "success", count: 0 ← silent no-op (the worst failure mode) verify : GDS reports actual = 40 ← belief revision from live evidence replan : re-sync rooms 41–50 ← autonomous recovery, no human in loop ``` **M
- Hacker News · Posted on Hacker News · First archived
Show HN: Teaching a 30ms local model, play browser games with LLM written rules ↗
Hacker News discussion.
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How to assess evidence about Artificial Intelligence
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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