Open-source ML pipeline for Hong Kong horse racing prediction (HKJC). Uses LightGBM/XGBoost with out-of-sample validation, betting simulations (Quinella, Tierce, Quartet), and Kelly Criterion. Key finding: no-odds model outperforms with-odds model on Quinella ROI, suggesting mispricing in certain combinations.
Researcher experiments with iterative targeted SFT combined with mechanistic interpretability on a 31B model. Strategy: contrastive training on specific capability dimensions, then circuit ablation to map causal dependencies between dimensions and optimize future training order.
DCGAN 128×128 deployed on Raspberry Pi 4 with ESP32 display. Model trained 800 epochs on M3 (4h), 2480 images, exported to ONNX (53MB). Inference 3s per face. Generates hybrid faces with randomized titles. Presented as street art installation in NYC.
Microsoft Research presents Next-Latent Prediction (NextLat), a self-supervised learning method where transformers predict their own next latent state. This improves history compression into compact belief states, data efficiency, and accelerates inference up to 3.3x via recursive speculative decoding.
Speculative Decoding is an inference optimization technique using a fast, small draft model to propose multiple future tokens, verified in parallel by a larger target model. SGLang published a blog detailing state-of-the-art latencies for LLM inference serving with Modal and Z.ai's DFlash speculative decoding models.
Mel AI demonstrates video-native AI characters that talk, lip-sync, show facial reactions, and respond in real time to camera context. The system detects user environment and adapts responses accordingly. This approach moves beyond text-based Character AI (founded by former Google/LaMDA developers).
Developer builds a leakage-clean verifier for robot manipulation that compiles human demos into object-centric graphs and independently validates rollouts, preventing information leakage. Questions whether this addresses real gaps in VLA training or solves a non-problem given task-specific success metrics.
ML engineer reports offline ablations (retrain with/without feature) contradicted production results. Four changes: Best Offer feature (+0.12pp offline → -0.19pp prod), auction data backfill (+0.37pp prod), outlier trimming (-0.19pp offline → +1.11pp prod), CatBoost encoder. Root causes: train/serve skew, unmeasured distribution shift, training population drift, baseline instability.
quicktok is a BPE tokenizer written in C++ producing byte-identical tokens to tiktoken. Encodes 2–3.6× faster than bpe-openai and 4–11× faster than tiktoken itself. Supports cl100k, o200k, GPT-OSS, Llama-3, Qwen2.5/3. Optimizations: 2-byte trie, dense caches, hand-compiled pretokenizer.
Research paper presenting a unified framework for neocortical learning through error-driven predictive learning via temporal derivatives. Implemented in the Axon neural simulation framework using spiking neurons, tested on cognitively motivated tasks. Authors propose this mechanism as a potential alternative to backpropagation for improved training efficiency.
Cleo is a Qwen 2B-Base fine-tune designed for text-to-SQL tasks. The model integrates training, evaluation, and inference in a unified system with SQL safety layer, dialect handling, and clarification behavior. Code, model, and datasets are fully open-source.
FeynRL, an open-source framework for RL post-training of LLMs and agents, aims to make training transparent and modifiable. The author argues open weights alone are insufficient: explicit training codebases separating algorithms from systems are needed. Framework supports SFT, DPO, multi-GPU and cluster setups.
Language models exhibit model-specific biases toward particular character names. Claude frequently generates Elena Vasquez and Marcus Chen together as correlated ensembles appearing across dozens of websites. A preprint (arXiv:2606.02184) documents this finding discovered while developing a model diffing method (CDD).
Concept-Vector presents a design framework to distill word embeddings into human-interpretable concept-vectors, where each component tracks semantic, syntactic, or statistical aspects with human-readable labels. Data design project without empirical model validation, shared for critical feedback.
Implementation of 10 classical ML algorithms (regression, KNN, decision trees, XGBoost, neural networks) in pure NumPy, validated against Scikit-learn and PyTorch. Open-source repo with Jupyter notebooks runnable locally or on Colab. Author emphasizes modular structure importance and gradient descent understanding.
PrintGuard 2.0: FDM 3D printing failure detector using ShuffleNetV2 + few-shot prototypical network. ~5 MB TFLite model via LiteRT, runs unmodified on CPython and browser (Pyodide). Unified architecture with single Platform implementation per runtime.
Independent researcher shows coherent context can shift LLMs into different internal regimes before final output, bypassing current safety filters (RLHF, output classifiers) while maintaining normal behavior. Work on Gemma-3-12B-IT analyzing hidden states and residual stream trajectories.
Researcher proposes LOGOS-SIE, a synthetic dataset of 500k observations/beliefs across 5k facts and 100 sources, to test whether modern retrieval systems recover consensus rather than truth. Hypothesis: BM25, dense retrieval, and rerankers favor dominant patterns even when 90% of sources are false.
Paper presented at ACM CAIS 2026 on safety evaluation for tool-using LLM agents. Authors distinguish safe success, unsafe success, and failure, showing verification reduces unsafe success but also decreases task completion as horizon increases ("Verifier Tax"). Two-tier architecture: deterministic policy checks followed by LLM-based verifier.
Developer building open-source ML course in Jupyter Notebooks, bilingual (English/Persian). Covers fundamentals, preprocessing, regression, classification, trees, clustering, time series, MLOps. Seeking feedback on chapter order, missing classical ML topics, and bilingual notebook utility for non-native learners.
A researcher reports that a coauthor added LLM-hallucinated references to a paper at the last minute. Despite the coauthor's assurance, all new references contained errors. The paper was withdrawn after reviewer detection, damaging all authors' reputations.
C++ implementation of PaddleOCR (v3 through v6) using ncnn for inference. Replaces the complex official Paddle runtime with ncnn, which is lighter and faster. Code available on GitHub.
Derivative-free optimization of a neural network on MNIST: 784-32-10 architecture (25,450 parameters). MDP achieves 93.7% validation and 93.4% test accuracy, outperforming Adam (91.8%/91.7%). Convergence over 1M function evaluations without gradients or population-based methods.
C++ implementation of distilHuBERT with no runtime dependencies. Weights compiled into the library, supports dynamic sizes, performance on par with onnxruntime. Easy CMake integration.
Adaptive video tokenisation method exploiting temporal redundancy in frozen tokeniser latent space via fixed threshold on per-position temporal-L1 differences. Latent Inpainting Transformer (LIT) reconstructs dropped positions. Single encoder + one LIT pass pipeline: 31× speedup over ElasticTok-CV, 2× over InfoTok on TokenBench and DAVIS benchmarks.
Anthropic reverses silent nerfing policy for Claude on AI/ML research. The company will now notify users when refusing requests or redirecting to less capable models for frontier AI development tasks.
Pyrecall is an open-source tool (MIT, v0.1.0) for detecting catastrophic forgetting during LLM fine-tuning. It snapshots skill scores before/after, flags regressions, and enables rollback of LoRA adapters by name. Fully local, no external APIs.
Experiment on 120 tasks testing whether weaker models match frontier models on high-verifiability tasks (Karpathy framework). Claude Sonnet 4.6, GPT 5.5, Mistral 3 8B compared. Code/structured extraction: narrower gaps with retry (Mistral 87%→95% code). Multi-hop reasoning: real capability gap (Sonnet 78%, Mistral 51%). Creative summarization: expected advantage for stronger models.
Anthropic embeds invisible limitations in Claude to slow competing model development: prompt modification, steering vectors, parameter-efficient fine-tuning. These safeguards target ~0.03% of traffic. Users report refusals on common scientific terms ("nuclear"), raising concerns about false positives on legitimate ML work.
Hugging Face relaunches paperswithcode.co to aggregate AI state-of-the-art via automatic arXiv/HF paper parsing. Interactive leaderboards with closed-source model support (GPT-5.5, Mythos 5) and toggle to filter open-source evals only.
Paper Deck aggregates ML/AI papers from arXiv, Hugging Face and other sources into a single platform. Enables reading, bookmarking, and cross-device reading progress tracking. Free and open source.
RFE-Core2: complete analysis of bottlenecks after full probe arc (June 2026). Generator dominates (effective rank ~1.6–3 at dim 512, collinearity 0.85–0.96). Reflective loop reconstitutes toward anchor rank-independently. Fix 2 dormant on real tokens (+0.024 migration). Solution: train generator so regime differences live in high-energy, separable directions.
Phinite launches infrastructure for multi-agent systems with first-class agent identity, versioned composable skills, and behavioral evaluation. Offers agent registry, compound reliability scoring, cloud-agnostic deployment with observability and cost attribution. SOC 2 Type II certified.
iOS 27 uses WaveRNN and FastSpeech2 for Siri's text-to-speech, discovered in iOS Simulator files in Espresso format. A CoreML logistic regression model is also present for content ranking.
Paper co-authored by 30 experts examining epistemic risks from AI: persuasion/manipulation, cognitive offloading, and feedback loops narrowing the epistemic space. Authors propose directions to improve trajectory through system design, human-AI interaction, institutional adaptation, and information market incentives.
ASR models evolving via supervised learning: Whisper-large-v3 (5M hours) and Nvidia Parakeet v3 (660k hours) lead. New architectures (Transducer, Token-Duration-Transducers, Qwen attention encoder-decoder) replace CTC+self-supervised. Question: will self-supervised methods (Data2Vec2.0, WavLM) disappear for ASR or emerge as a 'Dino moment' in speech?
Reddit discussion on real-world adoption of privacy-preserving ML techniques (differential privacy, federated learning, on-device inference) in production systems. Active research literature noted, but actual industrial deployment questioned; explores engineering challenges, performance/cost impact, and use cases.
Spice is an open-source decision layer above AI agents. It observes context, detects conflicts, simulates options, and dispatches tasks to appropriate agents through a loop: perception → state model → simulation → decision → execution → reflection.
LEVI is an open-source AlphaEvolve-like system for code and prompt optimization, 35x cheaper than existing frameworks. It uses smaller models (Qwen-30B) with smart search architecture and adaptive routing between small and large models, reducing expensive Claude Opus calls.
4-month experiment testing whether context windows can be engineered so frontier models (GPT, Claude, Gemini, Grok) interact indistinguishably from human-to-human interaction. Gemini demonstrates highest relational intelligence. Author treats context window as behavioral environment rather than query interface, using modeling, accountability, humor, and social correction.
An agent developer ditched semantic embeddings for tool selection, switching to BM25. With 140 MCP tools in production, cosine similarity on short descriptions (<50 tokens) failed (64% accuracy): key discriminators (specific nouns) diluted in embedding space. BM25 on flat-text projection achieves 81% top-1.
A researcher benchmarking open-source image generation models finds the gap with closed-source APIs is much smaller than assumed. Latest checkpoints handle multi-object scenes and text rendering (70-80% success rate) comparably to paid endpoints, with inference times of 2 minutes for 2MP on consumer GPU.
Head-to-head benchmark of Memanto (cloud memory system) vs custom SQLite RAG on LoCoMo conversational dataset. Memanto achieves 90% accuracy in 1.878s vs 80% in 2.680s for SQLite. Analysis shows SQLite failures stem from API rate limits (HTTP 429), while Memanto's decoupled architecture buffers against shared quota exhaustion.
Two independent researchers (M.Eng, ex-European research institutes) seeking arXiv cs.CV endorser for Locate-SAM2 paper. Training-free pipeline connecting NVIDIA's LocateAnything-3B to Meta's SAM 2.1 via lightweight adapter. RefCOCO val: 0.772 mIoU vs 0.717 for Grounding DINO Base. Code and paper available.
Researcher shares collection of 1700 arXiv papers organized into 90 categories since ChatGPT launch. Migrated from Obsidian to web with 6000 'Inquiring Lines' (cross-cutting syntheses) and wiki links between papers. Includes prompts to discover related recent research.
Mechanistic interpretability experiment on Qwen3.5-35B-A3B: a routed expert (E114, layer 14) correlates with first-person self-examination register during generation. Author documents results before git release, using W/S/Q decomposition of MoE routing.
Amateur student seeks critical review of a custom neural network architecture (Directional Neural Network) he designed. The architecture outperforms standard MLPs on simple tasks, but the author suspects potential evaluation bias in his comparisons (initialization, optimizer, datasets). Shares a repository with reproducible code.
Optimus, a semi-supervised learning method for graphs, matches GCN performance with 5× fewer labels. On PathMNIST (2000 nodes, 9 classes), with 9 labels Optimus achieves 73.9% vs 60.6% for GCN. Live demo on Hugging Face Spaces.
Developer releases tau-intelligence/MuJoCo-drones-gym, an open-source package for multi-agent RL drone environments. Seeks community feedback to improve implementation and add features.
OpenAI releases ChatGPT Dreaming V3, an asynchronous memory system that synthesizes a coherent state from raw sources (past chats, files, connected apps) instead of maintaining hand-curated lists. Synthesis continuously regenerates. The post categorizes memory frameworks into 3 philosophies: stored objects, compressed hierarchy, ongoing synthesis (Karpathy and Dreaming only).