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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 ↗6002 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.
- Hugging Face · Model / dataset date · First archived
Arushhh/c1b-judge-lora-sdar30b-b32 — model update ↗
# Phase-1 judge checkpoints (1B-token corpus, wave A) LoRA judge adapters for the stock **JetLM/SDAR-30B-A3B-Chat-b32 @ c351bbc** (block 32), trained with `code/corpus1b/train_c1b.py` from github.com/NoviceCoderInfinity/diffusion-moe-general-expert (branch `phase1-datacorpus`). The drafter they judge is the stock **JetLM/SDAR-1.7B-Chat-b32 @ 2c83c05**. Every folder ` / /` is a complete checkpoint: `adapter.safetensors` + `adapter_config.json` (LoRA r32 / alpha 64 on q, k, v, o in all 48 layers + the draft flag) and `trainer_state.pt` (AdamW state, token count) + `trainer_state.json`, so it can
- Hacker News · Posted on Hacker News · First archived
Leela Chess Zero – Self-Learning Neural Network ↗
Hacker News discussion.
- arXiv · Published · First archived
SteerProbe: Learning to Bypass Safety Steering in Vision-Language Models ↗
Activation steering offers an inference-time defense for vision--language models (VLMs) by modifying intermediate representations without updating backbone parameters. However, protection on benchmark inputs may not persist across alternative expressions of the same harmful request. We investigate this gap using fixed textual, visual, and joint reformulations designed to preserve the underlying intent, and find that these changes can bypass representative steering defenses. A complementary local analysis provides a sufficient condition under which a reformulation can cross a surrogate safety m…
- arXiv · Published · First archived
OceanMind: A multi-agent AI system for ocean diagnosis ↗
Time-dependent, three-dimensional (3D) oceanic multi-variables define coherent states of the evolving ocean to facilitate ocean diagnosis and advance ocean science to better inform environmental and hazard management. However, extracting quantitative evidence from these variables requires substantial and complex analytical effort. We introduce OceanMind, a multi-agent AI system that directly couples large language models (LLMs) with comprehensive time-dependent 3D ocean states for swift and effective diagnosis. OceanMind organizes the analytical process into four coordinated complexity stages:…
- GitHub · Repository created · First archived
nikolov9996/agent-friendly-smart-contract-audit-findings ↗
Agent-friendly smart-contract audit findings dataset with searchable Markdown records and structured metadata.
- Hacker News · Posted on Hacker News · First archived
What do you want from AI? ↗
Hacker News discussion.
- arXiv · Published · First archived
Trustworthy Runtime Error Healing in Real-World Repositories: A Benchmark and Guardrail ↗
Runtime error healing lets a crashed program continue by generating code that repairs its live runtime state. Recent work shows that LLMs can generate such healing code, but it is evaluated only on small competition programs, and executing LLM-generated code inside a live process raises safety concerns that remain unaddressed. In this paper, we take LLM-based runtime healing toward practical use in real-world repositories. We first build HealBench, a benchmark of 265 runtime errors from 18 real-world repositories, each paired with a reference execution on the patched version. HealBench also pr…
- arXiv · Published · First archived
RAGScope: A Leakage-Controlled, Cost-Aware Evidence-Gating Protocol for RAG Hallucination Triage ↗
Retrieval-augmented generation (RAG) systems need inexpensive ways to route generated answers: accept low-risk outputs, review uncertain ones, and reserve strong verifiers for the expensive tail. We present RAGScope, a leakage-controlled protocol for evaluating local evidence gates that use only the task input, retrieved context, and answer text. The protocol combines context-grouped splits, fold-scoped preprocessing, group bootstrap intervals, deployment operating points, end-to-end runtime, and explicit source-shift stress tests. On three RAGTruth tasks, the enhanced gate RAGScope-E reaches…
- Hacker News · Posted on Hacker News · First archived
Repeated VM Escapes by GPT-5.6-Cyber Based Agents ↗
Hacker News discussion.
- arXiv · Published · First archived
Can Agents Trust Their Skills? Uncovering Unsafe Chains of Trust in Skill-Based LLM Agents ↗
LLM agents increasingly rely on installable skills, which are packages of instructions, code, and resources that equip them with task-specific capabilities and, once installed, can be automatically invoked across subsequent user tasks. This creates a chain of trust in which users delegate authority to agents, while agent frameworks admit skill-provided content into the agents' context with insufficient validation, allowing malicious skills to influence agent behavior under that delegated authority. Yet, little is known about whether this trust model adequately constrains untrusted skill conten…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
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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