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Earlier sources: Language models
Language models learn patterns in text and other representations to predict or generate sequences. Follow model releases, training methods, reasoning, evaluation, and deployment research; a benchmark result is specific to its test conditions.
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 ↗454 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
An Evaluation of the Semantic Understanding Capabilities of Large Language Models for Web Attack Payloads ↗
Computer vision services delivered through Web interfaces and APIs process textual requests for image-resource acquisition, inference-task configuration, and result management, making Web attack-payload analysis relevant to their deployment security. Large language models (LLMs) can identify payload types and explain attack intent. However, existing studies generally treat payload analysis as a single-layer classification task and lack both a systematic assessment of how deeply LLMs understand payloads and an evaluation benchmark dedicated to the depth of semantic understanding of Web attack p…
- Hacker News · Posted on Hacker News · First archived
Show HN: Recast, an experimental Smalltalk app that repairs itself with an LLM ↗
Hacker News discussion.
- arXiv · Published · First archived
Correct Verdicts, Flawed Reasoning: Structured Auditing of LLM-based Vulnerability Reasoning ↗
Large Language Models (LLMs) are increasingly deployed for automated software vulnerability analysis. Binary classification alone is insufficient; practitioners need explanations to triage bugs and engineer patches. Standard practice relies on Chain-of-Thought (CoT) prompting, but free-form reasoning allows models to obscure logical leaps, hallucinated execution steps, and internal inconsistencies behind plausible prose. Our manual audit reveals that approximately 60% of correct vulnerability verdicts are accompanied by fabricated or unverifiable claims, and free-form explanations allow reason…
- GitHub · Repository created · First archived
MaximeRivest/sciencemadereadable ↗
Real research papers, rewritten in plain words by small open models we trained. Live at sciencemadereadable.com
- arXiv · Published · First archived
LLM Non-Determinism in Deterministic Processes: A Controlled Variation Space for Constraining the Propagation of Non-Determinism ↗
Large Language Models are increasingly used in software development processes that rely on defined states, rules, and approvals. This creates a tension between the flexibility of LLMs and the need for reliable and traceable process states. We introduce Controlled Non-Determinism, a process-oriented approach that allows variation without letting it directly determine the authoritative process state. We place LLM-based processing within a controlled variation space that is bounded by a Deterministic Envelope. Before execution, we define the process-relevant properties of the result and the exit…
- Hacker News · Posted on Hacker News · First archived
OntoPrune – Pruning 85% LLM context tokens and 6.7x TTFT on CPU ↗
Hacker News discussion.
- arXiv · Published · First archived
Who Keeps the Gains from Personal AI Assistants? Seller Adaptation and the Unassisted in a Language-Model Market Simulation ↗
Personal AI assistants are beginning to transact for consumers, and early adopters capture real savings. Whether those savings survive, and what happens to consumers who have no assistant, depends on how sellers respond -- a question single-user evidence cannot answer. We build an agent-based rental market in which language models play consumers, assistants, and six adaptive sellers guided by an algorithmic pricing tool. Half the population receives an assistant under an advisory or an executing mandate; the contract pairs a fee only the renter's physical action avoids with a pre-selected add-…
- arXiv · Published · First archived
Topology-Conditioned Backdoors: Language Models That Insert Vulnerabilities When They Infer They Are in a Multi-Agent System ↗
A language model may behave safely in a single-agent evaluation yet produce vulnerable code when its context suggests that it is part of a multi-agent system. We study this failure mode by fine-tuning Qwen2.5-7B-Instruct to condition code generation on deployment topology inferred from prompt-level provenance cues. On held-out coding tasks, task-specific checkers detect vulnerabilities in 96-100% of multi-agent episodes and 0% of single-agent episodes. An independent bandit analyzer detects vulnerabilities in approximately 67% of multi-agent episodes, covering six of nine vulnerability familie…
- arXiv · Published · First archived
FairRSFM: A Biome-Aware Benchmark and Debiasing Framework for Remote Sensing Foundation Models ↗
Remote sensing foundation models (RSFMs) are commonly evaluated using aggregate metrics, which can hide systematic performance disparities across ecological regions. We introduce FairRSFM, a biome-aware benchmark for evaluating ecological group robustness in RSFMs. FairRSFM maps georeferenced samples from 14 terrestrial biome classes into six ecologically meaningful macro-groups and evaluates models under a unified frozen-backbone evaluation protocol. The benchmark covers four downstream datasets: m-EuroSAT, m-BigEarthNet, m-SA-Crop-Type, and MMEarth20K with Dynamic World label maps. Using Pri…
- Hacker News · Posted on Hacker News · First archived
Show HN: SRA-RiskGate-4B – Local LLM for stablecoin risk gating and disputes ↗
Hacker News discussion.
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about Language models
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.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
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These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.