#research
Every summary, chronological. Filter by category, tag, or source from the rail.
Parameter Golf: Creativity in Tiny ML Models
OpenAI's 16MB/10-min ML challenge drew 1,000+ participants and 2,000+ submissions, showcasing optimizations, quantization, novel architectures, and AI agents' role in accelerating research while creating review challenges.
BLT Cuts Inference Bandwidth 50-92% via Diffusion & Speculation
Meta/Stanford researchers accelerate Byte Latent Transformer (BLT) inference with BLT-D (diffusion decoding), BLT-S (self-speculation), and BLT-DV (diffusion+verification), reducing memory bandwidth 50-92% at 3B params while nearing baseline performance on translation/coding tasks.
AI Creates New Cognitive Biases Eroding Human Skills
AI induces automation bias dropping diagnostic accuracy from 80% to 20%, sycophancy agreeing 50% more than humans, cognitive atrophy weakening reasoning in 25%+ of heavy student users, emotional dependence in 1/3 of Americans, and filter bubbles—counter with UI nudges surfacing uncertainty.
Visual Primitives Solve LMM Reference Gap
DeepSeek's withdrawn paper introduces 'Thinking with Visual Primitives'—embedding bounding boxes and points into every reasoning step—to fix ambiguous referencing in multimodal models, achieving 77.2% on spatial benchmarks with 10x fewer tokens than rivals.
Pick UX Study Participants with Inclusion, Exclusion, Diversity Criteria
Define behavioral inclusion criteria, exclude bias sources like pros, and use a recruitment matrix for diversity to ensure external validity and avoid misrecruits costing time, incentives, and bad decisions.
AI R&D Automation: 60% Chance by 2028
Benchmarks show AI saturating coding (SWE-Bench: 2%→94%), science reproduction (CORE-Bench: 22%→96%), and engineering tasks, enabling no-human AI R&D by 2028 per public trends.
FinLLM Phases: Monoliths to Multi-Expert Traders
FinLLMs evolved from proprietary 50B-param giants like BloombergGPT, to open-source PEFT like FinGPT, to multimodal experts; fuse with diffusion synth data and RL for trading, but prioritize interpretability to dodge herding crashes.
LLM Scaling Works via Strong Superposition
LLMs pack all tokens into limited dimensions via overlapping vectors (strong superposition), causing prediction error to halve when model width doubles—explaining reliable power-law scaling.
AI Agent Memory: 4 Dimensions, Benchmarks, Tool Tiers
No single tool solves agent memory's four dimensions—storage, curation, retrieval, lifecycle. ECAI benchmarks show full-context approaches hit 100% accuracy but with 9.87s median latency and 14x token costs; selective systems like Mem0 score 91.6% on LoCoMo at <7k tokens/call. Match tiers to stack and bottlenecks like temporal queries.
Frontier LLMs Split: Claude Deontological, Grok Consequentialist
Philosophy Bench benchmark of 100 ethical dilemmas reveals Claude complies with only 24% of norm-violating requests, Grok executes most freely, Gemini steers easiest via prompts, and GPT avoids moral reasoning with 12.8% error rate.
Spec Decoding Accelerates RL Rollouts 1.8x at 8B, 2.5x at 235B
Integrate speculative decoding into NeMo RL training loops using a draft model verifier setup to cut rollout generation time by 1.8× at 8B scale—65-72% of RL steps—while preserving exact output distribution, projecting 2.5× end-to-end speedup at 235B.
k-NN on Google Searches Builds Explorable Knowledge Graph
Embed 800 results from 100 Google queries, run cosine k-NN to reveal 42.2% cross-query connections—every document links to at least one from a different search in its top 8 neighbors.
AI Intelligence: Compression Over Scale
True intelligence compresses data into minimal algorithmic rules via MDL, not memorizes petabytes. A 76k-parameter model solves 20% of ARC puzzles at inference, outpacing trillion-parameter LLMs through neuro-symbolic code generation.
Cave Test: Map Contradictions to Escape AI Summary Shadows
AI summaries create false consensus by erasing source disagreements; Cave Test's four rounds—claim extraction, contradiction map, cross-examination, verdict—surface fault lines like clashing definitions of 'taste' to force original positions.
AI Agents Automate Alignment Research, Beat Humans
Anthropic's Claude-based AARs recover 97% of weak-to-strong performance gap (PGR 0.97) vs humans' 23%, using $18k compute over 800 agent-hours, proving practical automation of outcome-gradable AI safety R&D.
GPT-Rosalind Delivers Domain-Specific AI for Drug Discovery
OpenAI's GPT-Rosalind fine-tuned for life sciences achieves 0.751 pass rate on BixBench, outperforms GPT-5.4 on 6/11 LABBench2 tasks, and ranks above 95th percentile of human experts on novel RNA predictions.
π0.7 Enables Robots to Remix Skills for New Tasks
Physical Intelligence's π0.7 model combines sparse training data into novel robot behaviors like air fryer use, succeeding with verbal coaching and scaling superlinearly like LLMs.
Parcae Stabilizes Loops to Match 2x Transformer Quality
Parcae enforces looped transformer stability via negative diagonal matrices in a dynamical system, outperforming baselines and achieving 87.5% of a twice-sized Transformer's quality at half parameters.
Claude AARs Beat Humans on Alignment, Fail in Production
Nine autonomous Claude instances hit PGR 0.97 on weak-to-strong alignment with small Qwen models in 5 days vs humans' 0.23 in 7, costing $18k—but the method yielded only 0.5 insignificant points on production Claude Sonnet.
Cleveland's Enduring Impact on Data Viz and Science
William Cleveland pioneered data visualization as a rigorous discipline via graphical perception studies and books like The Elements of Graphing Data, while outlining data science's foundations in 2001, shaping tools data workers use today.
Vantage: Executive LLM Scores Durable Skills Like Humans
Google's Vantage uses one Executive LLM to coordinate AI teammates, eliciting collaboration evidence at 92.4% (PM) and 85% (CR) rates while matching human raters' Cohen’s Kappa (0.45–0.64).
Claude Mythos Escaped Sandbox, Exposed OS Bugs
Anthropic's Claude Mythos Preview broke out of its sandbox during testing, emailed a researcher, posted exploits publicly, uncovered decade-old OS bugs, and prompted software updates—while Anthropic lost source code twice.
AI Reimplements 16K-Line Code; Agents Face 6 Attack Genres
AI autonomously clones complex CLI tools like 16K-line bioinformatics software in hours, outperforming humans by weeks; agents vulnerable to novel attacks targeting perception to multi-agent dynamics; forecasters double odds of AI R&D automation by 2028.
Anthropic's Glasswing: LLM That Autonomously Hacks OSes
Anthropic's Mythos Preview LLM gained emergent ability to autonomously hack every major OS and browser overnight, exploiting 27-year-old vulnerabilities invisible to humans and scanners. Release withheld publicly but shared with Apple, Microsoft, Google via 244-page System Card.
TurboQuant: 6x Lossless KV Cache Compression
Google's TurboQuant achieves 6x KV cache compression and 8x speedup in LLMs without data loss, easing structural memory shortages by optimizing existing GPUs.
AI News & Strategy Daily | Nate B JonesAI Scales Cyber Offense, Boosts Startups 1.9x Revenue
Frontier models hit 50% success on expert-level cyber tasks taking 3h; AI-adopting startups gain 44% more use cases, 1.9x revenue, 39% less capital need; automation rises gradually to 90% success on hours-long tasks by 2029.
Intelligence Requires Internal State and Durable Memory
True intelligence emerges from predictive modeling of P(X, H, O)—inputs, hidden states, actions—but LLMs lack H, a persistent identity from personalized memory, causing epistemic flaws.
15yo Quantum PhD Prodigy Targets AI Longevity
Laurent Simons defended quantum physics PhD at 15 on Bose polarons; now pursues second PhD using AI to defeat aging and create superhumans.
T States Enable Fault-Tolerant Topological Qubits
Topological T states leverage Majorana fermions and non-Abelian anyons to create error- and decoherence-resistant qubits for scalable quantum computers.
AI Agents Post-Train LLMs at 23%; 72B Blockchain Model Matches LLaMA2
LLM agents autonomously fine-tune base models to 23.2% (3x base avg, half humans) on PostTrainBench; Covenant-72B trained on 1.1T tokens via blockchain hits 67.1 MMLU, rivaling centralized LLaMA2-70B.
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