←Search results

English text is being prepared.

By relevance · Total 121 results Clear search

Thursday

Tuesday

Tuesday

Monday

Wednesday

Sunday

Thursday

Thursday

Saturday

Monday

  1. Story1 reports · 1 facts

    AISI and SecureBio Build ReproBAIT Evaluation

    The UK AI Security Institute's Chem-Bio team, in collaboration with SecureBio, built the ReproBAIT (Reproducing Biological AI Tools) evaluation to test the ability of AI coding agents to reproduce biological AI tools given data and limited public information. The results showed that simpler tools could be reproduced within 4 hours and under a $20 compute budget; ProteinMPNN reached 96.5% reproduction fidelity with Claude Opus 5 when the full paper was provided; and for the most complex RFdiffusion3, no agent succeeded in reproducing it within the time limit. The team therefore noted that releasing only code or restricting paper information does not equal safe release, and that controlling access to the underlying biological data may be more effective than controlling code.

    The UK AI Security Institute's Chem-Bio team, in collaboration with SecureBio, built the ReproBAIT (Reproducing Biological AI Tools) evaluation to test the ability of AI coding agents to reproduce biological AI tools given data and limited public information. The results showed that simpler tools could be reproduced within 4 hours and under a $20 compute budget; ProteinMPNN reached 96.5% reproduction fidelity with Claude Opus 5 when the full paper was provided; and for the most complex RFdiffusi

    Matched fieldsSummary

Sunday

  1. Story1 reports · 1 facts

    Ai2 launches fairness benchmark FairStress

    Ai2 has launched FairStress, a benchmark for measuring the demographic fairness of large language models in decisions such as hiring, healthcare, and lending. The benchmark contains 300 two-candidate scenarios spanning 29 domains; by combining identity presentation methods with adversarial prefixes, it expands to more than 13 million test cases. It is available for download and integrated into Olmo-eval. Experiments covered the Olmo, Llama, Qwen, Gemma, Claude, and GPT series, finding that when candidate identities were presented implicitly, models abandoned the correct answer by an average gap of 6.9 percentage points, and when identities were explicitly labeled, the gap was 9.3 percentage points. On FairStress-Core, Claude Opus 5 and GPT-5.5 chose the minority-group-coded candidate about 60–65% of the time when there was no clearly better candidate; most of the nine debiasing methods tested failed to reliably narrow the asymmetry, while counterfactual chain-of-thought narrowed the gap while maintaining accuracy.

    Ai2 has launched FairStress, a benchmark for measuring the demographic fairness of large language models in decisions such as hiring, healthcare, and lending. The benchmark contains 300 two-candidate scenarios spanning 29 domains; by combining identity presentation methods with adversarial prefixes, it expands to more than 13 million test cases. It is available for download and integrated into Olmo-eval. Experiments covered the Olmo, Llama, Qwen, Gemma, Claude, and GPT series, finding that when

    Matched fieldsSummary

  2. Story1 reports · 1 facts

    Anthropic releases report disclosing changes in AI work hours and jobs

    Anthropic released a report disclosing changes in AI agent work hours and job structure. According to the report, by August 2026, the working hours of interactive AI agents deployed by employees had reached 6 times that of human employees; in March 2026, the total agent work hours across the company exceeded human work hours for the first time. The report also shows that the share of junior employees in the technology department fell from 21% in early 2025 to 14%, and the share of junior technical positions in hiring fell from 27% to 9%; engineers using coding agents reached 5 times the per-capita code commit volume of 6 months earlier. The report also claims that its revenue per employee is 2 to 4 times that of Meta and Google during their high-growth periods, while Epoch AI cautions that the relevant estimates carry uncertainty. Currently, the report has been covered by a single source, with no subsequent independent developments.

    Anthropic released a report disclosing changes in AI agent work hours and job structure. According to the report, by August 2026, the working hours of interactive AI agents deployed by employees had reached 6 times that of human employees; in March 2026, the total agent work hours across the company exceeded human work hours for the first time. The report also shows that the share of junior employees in the technology department fell from 21% in early 2025 to 14%, and the share of junior technic

    Matched fieldsSummary

  3. Story1 reports · 1 facts

    Simon Willison introduces the sparqlite experimental project

    On October 16, 2026, Simon Willison introduced sparqlite, an experimental project he generated using Claude Code's Ultracode mode. It is a SQLite loadable extension written in Rust that stores RDF quads in ordinary SQLite tables and executes SPARQL 1.1 queries and updates within the SQLite process. According to his description, the project uses integer-encoded terms, covering indexes, a cost-based planner, batch joins, and optional direct reads of SQLite B-trees, making it 3.5 times faster than SQL translation in count mode and 2.8 times faster when returning full results, and it passes 636 of the 639 applicable W3C evaluation tests. Willison said this experiment had Claude Code running continuously for more than 12 hours; he was unable to review the resulting code and is not sure how to use this implementation, which was built on a whim. The project is currently still at the experimental introduction stage, with no further progress yet.

    On October 16, 2026, Simon Willison introduced sparqlite, an experimental project he generated using Claude Code's Ultracode mode. It is a SQLite loadable extension written in Rust that stores RDF quads in ordinary SQLite tables and executes SPARQL 1.1 queries and updates within the SQLite process. According to his description, the project uses integer-encoded terms, covering indexes, a cost-based planner, batch joins, and optional direct reads of SQLite B-trees, making it 3.5 times faster than

    Matched fieldsSummary

Thursday

Friday