LLMs & Models
Large language models: GPT, Claude, Gemini, Mistral and open weights.
93 published articles
Reinforcement learning
Microsoft's experiential learning fix gives AI models a coach, not just a score
Experiential Learning repurposes the LLM-as-a-Judge into an LLM-as-a-Coach that extracts transferable knowledge from each response and internalizes it via on-policy context distillation, beating rubric-based RL on held-out tasks and reducing reward hacking.
2026-07-30
Mechanistic Interpretability
Gemma 4 knows physics well enough to steer it, and that changes everything
New research on gemma-4-E4B-it reveals that the model's internal representations of materials science mechanisms are causally linked to its answers. The study combines multiple probing and intervention techniques to show that physics knowledge is encoded in state transformations, not just static patterns.
2026-07-29
AI Research
Distributional RL heads make risk claims that are mostly false. A new audit proves it.
A large-scale audit shows that distributional RL agents systematically fabricate risk trade-offs at the very states where practitioners would most trust them. Across QR-DQN, C51, and IQN on MinAtar, zero of the strongest claims were confirmable, and acting on the agents' CVaR advice sometimes performed significantly worse than chance.
2026-07-29
Open Source AI
Nvidia's AV-Flamingo solves the one thing every other video AI gets wrong: time
Nvidia releases AV-Flamingo, an open audio-visual large language model designed for long, complex video understanding. It uses a three-stage curriculum and a timestamped chain-of-thought framework to handle temporal and cross-modal reasoning that trips up most models.
2026-07-28
Behavioral science
LLMs just bypassed the hardest part of behavioral economics: expensive surveys
A study using 3,000 GPT-5-based agents in route choice experiments shows LLMs reproduce cumulative prospect theory biases, loss aversion, reference dependence, probability weighting, with high fidelity. The approach bypasses the bottleneck of parameter estimation that limits traditional behavioral modeling.
2026-07-28
AI Models
Macaron-V1-Venti's 748B four-specialist design beats GPT-5.5 and Opus 4.8
Macaron-V1-Venti from MindLab Research leads benchmarks in personal intelligence, coding, terminal use, and Generative UI, using a novel Mixture of LoRA architecture that keeps the model compact while specialized.
2026-07-27
Open Source AI
Kimi K3 is the biggest open model ever. It's still not the best.
Kimi K3 is the largest open model at 2.8T parameters, but it fails to beat the best proprietary models on overall benchmarks. While it excels on specific coding and agentic tasks, the gap to frontier leaders like Claude Fable 5 and GPT 5.6 Sol exposes the limits of scaling without architectural and data breakthroughs.
2026-07-27
Agentic Finance
Why returns alone lie: NextFund opens the black box on AI trading agents
NextFund records every decision an AI trading agent makes in live markets, letting users compare models, inspect rationales, and diagnose failures. An eight-LLM test across US, China, and Hong Kong equities shows that similar intermediate signals can diverge into starkly different portfolio behaviors, and that quarterly rankings flip depending on which metric matters most.
2026-07-27
AI Research
The AI that tracks what students skip, not just what they click
MR-ConceptGCN combines personal knowledge graphs with multi-relational GCNs and SBERT embeddings to preserve the difference between a student's correct answers and skipped content. In a 31-person study, it improved recommendation accuracy, usefulness, diversity, and satisfaction over baseline approaches.
2026-07-26
Cost vs. capability
The $1.40 model that crushed its $2.82 sibling on physics
Four AI models were asked to build interactive destruction scenes with real physics. Opus 5 passed all three at the lowest cost among top performers, while Fable 5 failed each one at twice the price. A reminder that price tags don't predict physics.
2026-07-25
Agentic RL research
RL's sparse-reward blind spot meets SEED: agents write their own lessons
SEED (Self-Evolving On-Policy Distillation) lets an LLM analyze its own past trajectories, extract reusable natural-language skills from them in hindsight, and distill those lessons back into its policy during RL. The result: denser, on-policy supervision that improves success rates by up to 22% on long-horizon tasks.
2026-07-25
AI Models
Claude Opus 5 nearly matches Fable 5 across benchmarks, with cybersecurity as a deliberate blind spot
Claude Opus 5 launches with near-Fable-level scores on coding and knowledge benchmarks at half the price. But its deliberately weakened cybersecurity capabilities create a clear trade-off for developers.
2026-07-24