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5.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.7 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.5 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.8 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.4 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.4 VoiceCodeBench arXiv paper proposes benchmark for exact structured-token recovery in speech recognition — 1 src1.4 Arabic-Russian Parallel Corpus and LLM Benchmark for Scientific Text — 1 src1.4 Study Analyzes Self-Reported Limitations in NLP Research — 1 src5.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.7 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.5 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.8 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.4 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.4 VoiceCodeBench arXiv paper proposes benchmark for exact structured-token recovery in speech recognition — 1 src1.4 Arabic-Russian Parallel Corpus and LLM Benchmark for Scientific Text — 1 src1.4 Study Analyzes Self-Reported Limitations in NLP Research — 1 src
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#vlm

7 curated events
papersTODAY 04:00 UTC

Paper Details Option-Aware Retrieval and VLM Tuning for Offline Medical VQA

An arXiv paper describes a submission to the MedReason 2026 challenge that handles both multiple-choice and open-ended medical visual question answering with fully offline, containerized inference. The authors report that retrieval for multiple-choice questions needs to compare candidate options in a specific way, and they also adapt a vision-language model to the clinical task. The work is listed under both cs.AI and cs.CL.

medical-ai/vlm/retrieval/benchmark
papersTODAY 04:00 UTC

DiffAdapterVLA: Planner-Integrated Backbone for Driving VLMs

A new arXiv paper introduces DiffAdapterVLA, a method that folds continuous trajectory planning directly into the backbone of a pretrained driving vision-language model. The authors argue that while driving VLMs absorb rich visual, route, language and driving context, their training objectives stay disconnected from continuous planning, so prior work tends to bolt planning on separately. The approach targets native continuous trajectory generation rather than a detached planning head.

autonomous-driving/vlm/diffusion/arxiv
papersTODAY 04:00 UTC

NoteVQA Benchmark Targets Everyday Visual Questions From Human Communities

Researchers introduce NoteVQA, a benchmark that evaluates vision-language models on visual questions drawn from real human communities rather than pre-defined task categories. The work argues that current benchmarks focus on narrow capabilities such as multi-hop retrieval and miss the variety of questions users ask in daily life, including consumer AI search. It is published as an arXiv preprint.

benchmarks/vision-language/evaluation/vlm
papersSEP 10 04:00 UTC

ViSR-KGC: Vision-Language Model Approach to Multimodal Knowledge Graph Completion

Researchers have introduced ViSR-KGC, a method that uses vision-language models to reason over visual subgraphs when filling in missing entities or relations in knowledge graphs. The approach extends knowledge graph completion to multimodal settings by combining textual graph structure with information from entity-associated images. The work is detailed in a revised preprint posted on arXiv.

knowledge-graphs/multimodal/vlm/research
papersSEP 10 04:00 UTC

Two-token features and small-large VLM ensembles for hallucination detection at SHROOM-Visions 2026

Researchers present their system for the SHROOM-Visions 2026 shared task, which targets character-level detection of hallucinations in vision-language model outputs. The method fine-tunes a 4-billion-parameter VLM as a per-token classifier that reads a two-token feature from its own hidden states, then combines it with larger models in an ensemble.

hallucination/vlm/benchmark/ensembles