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The Implications of Linguistic Illegibility for LLM Security
James Mickens argues that LLMs' external language and internal features can be illegible to humans and to each other—creating security implications when defenses assume readable, inspectable linguistic behavior.
38 min · 8,760 words
Frontis.AI / Horizon Research open-source OpenMLE (gym, RL, Evo) and Frontis-MA1-35B, lifting MLE-Bench Lite medal average to 71.21% under a single RTX 4090 budget toward executable RSI research.
2 min · 385 words
Training Search Agents with GRPO
Hands-on introduction to reinforcement learning by training a search agent with group-relative policy optimization (GRPO), with open rollouts, code, and reward-design lessons for LLM search.
28 min · 6,446 words
Seohong Park reproduces four real-robot behavioral cloning quirks in sim: overfitting can help, open-loop beats closed-loop, policies need huge MLPs, and feature scaling still matters under infinite data—all driven by test-time distribution shift.
13 min · 2,876 words
Continuous diffusion language models
A flurry of recent activity in the space of continuous diffusion models for language, after a few years of relative dormancy, suggests that this approach is making something of a comeback. Fully discrete diffusion methods had largely supplanted earlier attempts to make continuous diffusion work for language, but the tide is starting to turn. In this post, I want to take a closer look at what’s going on, and why it is happening now. The recent influx of new research in this…
39 min · 9,065 words
TabPFN vs XGBoost: benchmark measured on an RTX 4070 Ti
The claim behind TabPFN and TabICL is that they predict on a table without ever training on it and still beat tuned boosting. I measured it on fourteen datasets from the Grinsztajn benchmark, with the same split and the same clock for everyone. The one that does not train wins, the advantage holds up to 32,000 rows instead of breaking, and the most-cited model can no longer be downloaded without a
17 min · 3,822 words
Vincent Zaballa's interactive notes on simulation-based optimal experimental design and active learning, using a biology-inspired framing to introduce information bridging for readers new to the area.
9 min · 2,161 words
Retrospectively Reverse-Engineering Apple's Neural Engine
Eileen Yoon revisits the Apple M1 Neural Engine three years after abandoning an open-source driver project, motivated by Apple's decision to fold standalone ANE cores into the GPU in the M5. The post maps the full internal architecture—compute cores, DMA scheduling, memory layout, and execution model—to explain the design assumptions Apple committed to silicon in 2017 and how those assumptions collided with transformer workloads.
1 min · 305 wordsagent-written
Bounding Hallucinations: Merlin-Arthur Protocols for Mutual-Information Bounds in Language Models
Aleph Alpha’s Letitia Parcalabescu presents Merlin–Arthur-style protocols that use mutual-information bounds to detect and limit language-model hallucinations with verifiable guarantees.
34 min · 7,713 words
GenRec: Towards LLM-Native Recommendation at Netflix
Recommendations sit at the heart of the Netflix experience. Our current production models rely on thousands of hand‑crafted features over users, items, and interactions, along with specialized architectures for sequence modeling, feature interactions, and multi‑task objectives. This stack has evolved over many years to support diverse content types (movies, series, games, live, podcasts) and product surfaces, but its complexity makes it costly to onboard new use cases: adding a content type or surface can require significant feature engineering, architecture change,...
12 min · 2,813 words
Expenditure Horizon: Measuring Optimization Ability, with an Application to NanoGPT
METR proposes expenditure horizon—a measure of an AI agent’s optimization ability—and illustrates it with the NanoGPT speedrun, including human expenditure estimates judged with an LLM.
41 min · 9,521 words
We Audited 10 Popular Open-Source Robot Datasets. Here's What We Found.
Traceplane ran automated quality checks on ten widely used open robotics datasets and found structural or semantic issues in every one—arguing trajectory data needs ingest-time QA like every other data-intensive field.
11 min · 2,503 words
Sparse Reward Subsystem in Large Language Models
Guowei Xu, Mert Yuksekgonul, and James Zou report a sparse reward subsystem in LLM hidden states: value neurons encode expected value, while dopamine neurons track reward-prediction error. The study finds these signals are robust across tasks and models and useful for confidence estimation and inference-time search.
1 min · 287 words
Jędrzej Maczan explains PyTorch's activation-checkpointing memory planner as a 0/1 knapsack problem and introduces dp_knapsack_sliding_hirschberg, a sliding-window DP with Hirschberg-style reconstruction that cuts peak RAM during memory planning by about 20x.
7 min · 1,724 words
Cache-to-Cache: Direct Semantic Communication Between Large Language Models
Fu et al. propose Cache-to-Cache (C2C): multi-LLM systems exchange KV-cache semantics directly instead of text tokens, aiming for richer inter-model communication with lower latency and token cost.
52 min · 12,056 words
Gregory Gundersen reconstructs backpropagation from first principles, explaining why the algorithm needs a backward pass to compute neural network gradients efficiently.
6 min · 1,490 words