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AutoBenchmark: benchmark creation & the role of humans
*Figure 1. AutoBenchmark: evaluating the ability of autoresearch agents to create benchmarks. AutoBenchmark instructs an autoresearch agent to create a benchmark, we specifically target benchmarks that evaluate autoresearch agents themselves (i.e., benchmarking autoresearch). Within the benchmark creation loop, the autoresearch agent iteratively revises the benchmark via two main sources of feedback: one from the benchmark solvers (including the same model used as the benchmark creator) and the other from external verifiers of the benchmark (AI or human). We thus also…
16 min · 3,723 words
Tokenization: A Survey for Modern NLP
A comprehensive survey by 32+ tokenizer researchers covering subword algorithms, multilingual pitfalls, evaluation, theory, security, and alternatives—framing tokenization as a first-class bottleneck in the LLM era.
2 min · 376 words
SynthID Bio: Watermarking methods for synthetic biology
Introducing SynthID Bio Proof of concept for watermarking AIgenerated proteins while preserving biological function. Today, we’re introducing SynthID Bio to bring watermarking technology to synthetic biology.
6 min · 1,276 words
Comparing Muon, NorMuon and AdamW for Fine-tuning a Dense Retriever
Qingcheng Zeng gives Muon and NorMuon the same tuning budget as AdamW when fine-tuning a contrastively pretrained dense retriever: lower training loss, no BEIR win. Learning rate and transfer matter more than the optimizer.
5 min · 1,151 words
GLM-5.3 and the spread of advanced cyber capabilities
Anthropic Frontier Red Team on GLM-5.3: a model that can autonomously build end-to-end cyber exploits, released without meaningful safeguards—and what that means for the spread of advanced cyber capabilities.
8 min · 1,815 words
Has AI impacted the labor market yet?
Alex Imas and Jacob Schaal survey the empirical evidence on whether generative AI has already shifted employment, wages, and task composition—separating strong claims from what the data currently support.
23 min · 5,183 words
Can New Technologies Finally Replace Animal Testing?
Brandon Keim reports for IEEE Spectrum on new approach methodologies such as organ-on-a-chip devices, stem-cell-derived tissues, organoids and AI models that aim to replace animal testing in drug development, how researchers are proving they work, and the regulatory and cultural shifts needed for wider adoption.
13 min · 3,009 words
Language Models for Text Classification: From Bag-of-Words to JevA visual guide to bag-of-words, RNNs, CNNs, transformers, Jev-like APIs, and calibration
Sebastian Raschka walks from classic bag-of-words classifiers through RNNs, CNNs, and transformers to TypeSafe AI's Jev—explaining APIs, IMDb benchmarks, calibration, and why decision models matter for agent harnesses.
5 min · 1,076 words
Responsible Release of AI-Generated Mathematics
The Advisory Group on Mathematics and AI (Sep 29, 2026) recommends how frontier labs should release AI-generated math results: deposit promptly, cite related work, formalize where possible, disclose prompts and costs, and fund community-led human understanding.
2 min · 559 words
Americans’ Social Activity Has Steeply Declined in the Past 30 Years
Data for Progress replicated 1995 DDB Needham survey questions with 1,098 U.S. adults and found double-digit declines in participation in many everyday social activities since 1995, with a few gains such as internet use, home video games and exercise classes, plus caveats about comparing different survey methods.
3 min · 725 words
Automatic Transmission — a data-privacy study of connected vehicles
Northeastern researchers (with Consumer Reports) present a large-scale measurement of the connected-vehicle ecosystem: 21 late-model cars and 30 companion apps, third-party trackers, disclosure gaps, and why vehicle data sharing needs more transparency.
7 min · 1,626 words
CLMs treat context as an editable file so models natively manage their own context—beating harness baselines on BrowseComp-Plus and long-horizon coding with fewer FLOPs, plus in-context skill evolution and RL for context management.
3 min · 699 words
Your car is collecting more data about you than you thinkNortheastern researchers tested 21 vehicles and companion apps for third-party data sharing
Northeastern cybersecurity researchers, with Consumer Reports, found connected cars and OEM apps sending VINs, location, and emails to advertising and analytics third parties—and call for clearer opt-in defaults.
2 min · 414 words
How we found 24 Android vulnerabilities using our open source AI security agent
GitHub Security Lab explains the targeted AI taskflows behind 24 Android findings, the bugs they uncovered, and how to run the same open-source Taskflow Agent on your own app.
10 min · 2,349 words
Harness Learning Enables Generalizable Test-Time Adaptation
arXiv paper introducing harness learning: a proposer model revises an agent’s executable harness from execution feedback, improving test-time adaptation on unseen reasoning and multi-hop QA without weight updates.
56 min · 12,765 words
Historians often blame drought, famine, and invaders for the Late Bronze Age collapse—but that can’t explain why the empires never came back. Patrick Fitzsimmons argues for a deeper structural story.
14 min · 3,253 words
What if automating AI R&D triggers an intelligence explosion?Frontier AI Working Paper Series No. 2/2026 from CASP and collaborators.
A CASP Frontier AI working paper asks what happens if automating AI R&D triggers an intelligence explosion, surveying mechanisms, uncertainties, and policy implications with coauthors across academia and frontier labs including Hinton, Bengio, Horvitz, Clark, and others.
43 min · 9,930 words
Towards safety cases for frontier AI training
OpenAI argues frontier RL runs should require structured safety documentation approaching “safety cases”: technical safeguards, operational practices, and incident investigation before continuing training.
7 min · 1,571 words
Meta FAIR introduces RL-XAR (Reinforcement Learning from eXpert-Aligned Rubrics): learn rubrics from the gap between expert writing and model output, then train models toward expert-level text generation to reduce AI slop.
16 min · 3,572 words
Can a Model Learn New Skills as Add-Ons?
Connito Research trains residual MoE experts with their own routers on a frozen DeepSeek-V2-Lite base, then merges independently trained math, code, medical, law, and finance experts in seconds without retraining—lifting domain benchmarks while leaving the original model untouched.
4 min · 913 words