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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 ↗450 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
SpaTime: Streaming Vision-Language Models for Spatio-temporal Reasoning ↗
Embodied agents must reason about 3D space while the video is still arriving, answering questions as soon as they have observed enough of the scene. VLMs that incorporate 3D geometric priors achieve strong spatial reasoning, but they operate offline, i.e., the full video must be available before they produce an answer. Streaming VLMs process frames causally and decide for themselves when to respond, yet they lack explicit 3D representations. We present SpaTime, a streaming VLM that fuses causal geometry tokens into the language model at every frame, using only the frames observed so far. To su…
- arXiv · Published · First archived
Agreement Is Not Validity: Cross-Model LLM Consensus in Diagnosing Student Failure Modes in K-12 Math Tutoring Dialogue ↗
In K-12 mathematics tutoring, student-tutor dialogue provides rich evidence of learners' problem-solving processes and sources of difficulty. Learning analytics research increasingly relies on large language models (LLMs) to extract such information from dialogue for a variety of downstream tasks, including knowledge tracing, behavioral modeling, and diagnosis of student reasoning errors. However, the validity of these model-generated interpretations remains insufficiently understood. In this exploratory study, we examine the validity of LLM classifications of five student failure modes in mat…
- arXiv · Published · First archived
GeneICL: A Tabular Foundation Model for Bulk Transcriptomics ↗
Gene expression is widely measured in biomedicine, yet clinical outcome prediction remains challenging due to high dimensionality, strong feature correlations, and limited labeled data. Large self-supervised transcriptomic foundation models often fail to outperform simple supervised baselines. Tabular foundation models offer an alternative through in-context learning, but are typically pretrained on generic synthetic data rather than transcriptomic structure. We ask whether transcriptomics-aware pretraining, rather than scale, is the missing ingredient. Towards this end, we introduce GeneICL,…
- arXiv · Published · First archived
Secure Speculative Decoding for Large Language Models ↗
Speculative decoding accelerates inference for a large language model (LLM), referred to as the \emph{target model}, by first using a smaller model, referred to as the \emph{draft model}, to generate candidate tokens and then verifying them with the target model for acceptance or rejection. Prior studies primarily focused on the efficiency-utility trade-off of speculative decoding, e.g., lossy speculative decoding, leaving its security implications largely unexplored. In this work, we bridge this gap by providing the \emph{first} systematic study of the security implications of speculative dec…
- GitHub · Repository created · First archived
mmlong818/open-academic-paper-gen ↗
Multi-agent pipeline that turns a research topic into a draft paper grounded in real literature, with citation hallucination detection and claim-support checking.
- Hacker News · Posted on Hacker News · First archived
Ask HN: How come everyone is an LLM expert? ↗
It is a bit strange how new model is released and hour after there is commentators declaring it complete trash and embarrassment to the AI industry, or the best thing since sliced bread. Surely they have not had the opportunity to test the ins and outs of the model yet? Or do people just blindly trust benchmarks as if they were not pretty easy to manipulate, as research has shown quite a few times now? Or is it just all vibe? So how do you measur…
- Hacker News · Posted on Hacker News · First archived
Your Language Model Is Not-So-Secretly a System 1 Model ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
bezzam/omniasr-llm-300m-v2 — model update ↗
# Model Card for Model ID ## Model Details ### Model Description This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] - **Repository:**
- arXiv · Published · First archived
Rapid Sequence Cost Evaluation for Multi-Spacecraft Rendezvous: A Permutation-Invariant Set Transformer ↗
Multi-spacecraft multi-rendezvous mission design requires repeated evaluations of the total cost of visiting a target set within a given time window, and the efficiency of this evaluation is the primary barrier to global search. Existing methods either optimize the order and epochs of each candidate sequence, which is expensive, or estimate the cost leg by leg, in which case the error accumulates with the sequence length. This paper proposes a deep model that regresses the total cost of an unordered target set directly: the pairwise transfer cost surface of each target pair is compressed into…
- arXiv · Published · First archived
Case-Level Verification in Scanner-LLM Cascades: Overcoming the Alert Aggregation Bottleneck to Expand the FRR-TPR Trade-off Space ↗
Dynamic Application Security Testing (DAST) scanners achieve high recall but also produce a large number of false positives, resulting in substantial manual triage costs. Large Language Models (LLMs), when used for independent detection, achieve extremely high recall (95.4%-100%) but also exhibit prohibitively high false positive rates (49.6%-85.0%), precluding their use as standalone replacements for scanners. A natural solution is a two-stage cascade consisting of scanner detection followed by LLM verification. However, a verification-granularity issue that has long been overlooked in practi…
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.
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