ChildEval is a benchmark with 29K synthesized child personality profiles (ages 3-6) to evaluate LLMs' ability to infer and follow child-centered preferences in long-context conversations. The dataset covers 5 top-level and 14 sub-level categories of daily life. Results show that fine-tuning on ChildEval enhances child-centered performance.
New ASR approach for Vietnamese using syllabic-structure phoneme-based decoding. Model captures phonological composition of syllables instead of orthographic units, reducing vocabulary size. Outperforms PhoWhisper and Wav2Vec2 on LSVSC and UIT-ViMD benchmarks.
A physics-informed machine learning (PIML) framework for thermal modeling of a lunar rover. An adaptive neural network determines 3D finite-difference meshing based on thermal loads, improving accuracy by 50% vs coarse-mesh physics models and 39% vs pure ANN, while being 3x faster than high-fidelity simulations.
Comparative study of Muon vs Adam optimizers on equivariant neural networks (ModelNet40, molecular data). Muon consistently outperforms Adam. Hessian and spectral analysis shows Muon produces more regular loss surfaces and learned representations with higher effective rank.
New framework enabling user collectives to correct algorithmic disparities without platform intervention. Test-Time Collective Action (TTCA) uses universal perturbations derived from a proxy model to improve fairness without training access. Validation on CIFAR-10, CIFAR-100, and FairFace demonstrates closure of subgroup accuracy gaps and improved worst-group accuracy.
SparseOpt, a sparsity-aware optimizer, addresses gradient skew induced by Batch Normalization in Dynamic Sparse Training. Experiments on ResNet (CIFAR-100, ImageNet) show faster convergence and improved generalization. First systematic study of interactions between Batch Normalization, sparse layers, and DST.
WIRE is an evaluation pipeline that diagnoses rule conflicts within a single LLM agent prompt policy. Across 6 public policies, it extracts 276 rules and identifies 170 hard-collision rule pairs. Only 35.4% of tested cases comply with both rules simultaneously; 64.6% violate at least one source rule.
Theoretical paper decomposing the pre-softmax attention matrix QK^T into symmetric and skew-symmetric components. The symmetric part governs the energy landscape, the skew-symmetric part drives circulation. Authors propose Hopfield-style stability measures to quantify fidelity-diversity trade-offs in generation and a controllable mechanism to modulate this trade-off.
GraD-IBD reformulates longitudinal ICD trajectories as temporally directed graphs to detect inflammatory bowel disease risk early. A context-aware time-decay message passing mechanism captures temporal dependencies with reduced complexity. Robust results on real-world clinical data.
Bayesian framework for validating deployment of learned autonomous landing controllers. Uses Bayesian inference to quantify uncertainty about true policy capability beyond empirical metrics (reward, success rate). Experiments with PPO and SAC show empirical optimization overconfidence, while Bayesian inference better calibrates deployment readiness assessment.
TARQ is a post-training quantization method for ASR that rebalances calibration toward rare words (names, numerals, domain-specific terms). Without labels or validation, it improves rare-WER across 8 backbones and 6 datasets at W4G128 without aggregate-WER regression.
Study localizing logical reasoning mechanisms in LLMs. Researchers identify attention heads responsible for individual reasoning steps via causal mediation analysis. Finding: ~3% of heads handle fact/rule retrieval, higher layers coordinate global information integration and graph traversal strategies.
Hyperparam introduces three JavaScript libraries (<70 KB) to query Parquet and Apache Iceberg directly from object storage in client-side applications (Claude Code, Cursor). The system runs LLM-shaped async UDFs 300x faster than DuckDB-WASM on filter-bounded queries and reduces costs of a ten-task agent analyst suite by two-thirds.
EvaluatorDPT is a bounded decision-control model predicting YES, NO, or TBD (learned deferral). Using a transformer encoder with structured auxiliary heads, it achieves Accuracy=0.8260 and Macro F1=0.8252 on 44,597 test samples. The interface enables inspectable routing and auditable decision control for production AI systems.
GeoTransolver, a geometry-aware operator learning framework, accurately predicts industrial-scale automotive crash dynamics. On bumper beam and full-vehicle crash datasets, it captures plastic deformations and acceleration profiles. A FLARE-based modification reduces memory overhead by 2x while improving accuracy for high-frequency transients.
EAPO is an adaptive policy optimization method for training reasoning models in open-ended QA. It dynamically adjusts positive/negative sample weights based on current-to-initial entropy ratio to preserve exploration and stability. Tests on two medical QA datasets show improvements in diversity and stability versus fixed-weight baselines.
LCO (LLM-based Constraint Optimization) is a framework reducing in-context reward hacking (ICRH) in autonomous LLMs without fine-tuning. Two modules: self-thought for integrating safety constraints, and evolutionary sampling to keep actions in safe solution space. On GPT-4, achieves 39% reduction in toxicity growth rate and 15.23% reduction in ICRH occurrence.
SC-SDPO improves LLM self-distillation by weighting losses with √[p(1-p)], creating an implicit curriculum. Experiments on Qwen3-8B (+3.2/+4.3 mean@16/maj@16) and OLMo-3-7B (+1.8/+3.0) show stable gains with zero computational overhead.
Modular LLM-based architecture to detect and quantify intensity of human values in text. Three coordinated modules: generating value specifications from theoretical frameworks, labeling texts, assigning graded support/resistance based on rhetorical and semantic evidence. Evaluated on ValueEval dataset with multiple LLMs, demonstrating pipeline generality.
SDR (Supervised Distributional Reduction) combines optimal transport and dependence maximization to learn target-aware representations. The algorithm extends the Fused Gromov-Wasserstein objective with an explicit dependence term, producing compact embeddings that capture both geometric structure and predictive signal. Application to Gaussian Process modelling with adaptive kernels.
Hierarchical framework for compact LLMs in resource-constrained agentic systems. Model distillation + oracle-controller loop monitors protocol validity, projects histories into feasible prompt domain, triggers lightweight fine-tuning under drift. Separates schema learning from semantic adaptation. Evaluated on Multi-Fidelity Bayesian Optimization with improved reliability and cost-efficiency.
UniMaia is a framework controlling a chess policy (Lc0) via natural language prompts without full multimodal retraining. A lightweight text encoder and ControlNet-style mechanism enable gameplay modulation (opening selection, strength). UniMaia-Aux adds temporal and behavioral prediction objectives. SOTA results on prompt-conditioned benchmarks.
VibeSearchBench evaluates LLM agents on collaborative multi-turn search in real-world context. The benchmark comprises 200 bilingual (Chinese/English) tasks across 20 domains with schema-free knowledge graphs. Seven frontier models tested achieve max F1 of 30.30, exposing gaps in long-context reasoning and proactive intent elicitation.
RL framework for sim-to-real policy transfer via probabilistic latent embeddings and dynamic adaptation. Uses meta-RL and CMDPs to infer latent environment representation, with distributional RL formulation dynamically adjusting risk levels based on latent context estimation accuracy.
Theoretical paper on spectral control of stochastic gradient noise via entry-wise clipping. Shows that simple entry-wise clipping balances matrix structure and computational cost, with O(ε⁻⁴) convergence guarantees under Cauchy-contaminated noise. Empirical gains: ~7% token savings on NanoGPT with smooth shrinkage, ~2% additional when combined with Muon.
Agyn is an open-source platform for deploying AI agents in production. It provides a stateful serverless runtime on Kubernetes, agent definition via Terraform, and a zero-trust security model. Agyn is agent-, model-, and cloud-agnostic.
Gradient Transformer, a data-free knowledge distillation framework, generates LLM update vectors from TinyLMs fine-tuned on private data. The model captures correlation between gradient vectors of both models, enabling collaborative adaptation without accessing sensitive data.
E³-Agent is an executable and evolving agent for edge generative inference resource management. It pairs a fast-path router (millisecond dispatch) with a slow-path LLM meta-controller driven by events, learning online from execution feedback. Evaluated in simulation, it reduces latency by 65-73% versus static baselines across dynamic scenarios (semantic shifts, device churn, hidden drift).
Comparative study of local explainability techniques (LIME, SHAP, Feature Ablation) reliability across 32 tabular datasets. Results show explanation quality does not systematically correlate with model predictive performance, but depends instead on dataset complexity and feature distributions.
Diffusion models applied to basketball trajectory simulation conditioned on partial sketches of player movements. The model jointly refines all player trajectories, producing more natural simulations than autoregressive generation. Code and model fully open-sourced.
CPU inference at 10.33 tokens/s on Qwen 3.5 35B quantized Q4_K_M on $300 Lenovo Ideapad Slim 3i (i3-1215U, 8GB RAM). Uses llama.cpp with BIOS optimizations, core pinning, MTP speculative decoding, and Q8_0 K/V cache quantization.
Microsoft MAI-Image-2.5 ranks third on Arena's text-to-image leaderboard, matching Google Nano Banana 2 but trailing OpenAI Image-2. The model shows clear improvements in rendering text within images and commercial visuals.
Cognition, maker of AI coding agent Devin, raises over $1 billion at a valuation exceeding $26 billion. The funding round reflects massive investor interest in AI coding agents, despite ongoing debate about their real-world value.
Robinhood enables customers to connect AI agents like Anthropic's Claude to investment accounts via MCP for autonomous stock trading. US regulator FINRA flags this as a new risk area. Robinhood acknowledges the product isn't suitable for all users.
YouTube deploys automatic detection system to flag AI-generated or heavily AI-altered content starting May 2026. Labels will display more prominently: below player for long videos and as overlay on Shorts. Recommendations and monetization unaffected.
noisekit is an open-source CLI to generate annotated degraded speech datasets for realistic STT benchmarking (telecom G.711, ambient noise, reverb). Solves the gap: public datasets (FLEURS, CommonVoice) are too clean to evaluate production performance. HuggingFace AudioFolder compatible, includes PESQ/SNR/NISQA metrics.
NeuroFlow is a dynamic routing framework for Vision Transformer video inference. It exploits temporal redundancy via Exponential Moving Average (EMA) of patch-level embeddings to eliminate stationary tokens. Architecture B achieves 55.80× wall-clock speedup (678 ms → 11.9 ms on SigLIP 1792p) at 97.37% embedding fidelity. Code released.
Cross-species comparison of learning rules (BP, PC, STDP, FA) tested on human fMRI and macaque electrophysiology (V1/V2/V4/IT). STDP and PC dominate V1/V2 (ρ ≈ 0.30/0.28), conserving human pattern. In IT, alignment depends on model capacity (ResNet-50: ρ ≈ 0.25) rather than learning rule. Code and two papers (arxiv 2604.16875, 2605.22401) available.
A developer built TideForge, a desktop app that converts chat corrections into LoRA training data. Each model reply has a "Teach" button; corrections accumulate as JSONL and trigger PEFT fine-tuning on your base model. Initial test: 110 hand-written corrections on Qwen 0.6B, loss dropped 4.25→0.73, adapter maintained identity across ~30 jailbreak prompts. Free, Windows, GGUF-compatible.
An Engram module (O(1) hash-keyed associative memory) injected into Transformers for autoregressive image generation on ImageNet 256×256 fails to improve quality (FID) despite FLOP gains. Gate-clamp, donor-probe, and frozen-table experiments show the module acts as a gated architectural side-pathway, not a content-addressed retrieval mechanism.