FETCH, an automated legal triage classifier, generates follow-up questions using a low-cost LLM ensemble. The study shows cheap models perform well at classification, but high-quality plain-language question generation requires GPT-4 or higher. Prompt engineering alone is insufficient; LLM-as-judge ratings diverge from human evaluations.
Survey paper proposing Intelligent Computing Architecture Model (ICAM), a six-layer framework for model-native computing. Maps classical computer architecture concepts to LLM systems (cache management, context, agents). Introduces three design laws: Semantic Locality Law, Context Budget Law, Agent Speedup Law. Distinguishes probabilistic execution plane from deterministic control plane.
Hybrid framework coupling LLMs with physics-based simulation for inorganic material synthesis planning. Case study on niobium-oxygen system: LLM-generated synthesis routes outperform classical path-planning algorithms by leveraging implicit priors.
TCAR-Gen combines query-conditioned graph neural networks, temporal evidence fusion, and chain-of-trees reasoning for retrieval-augmented generation. Achieves 0.3738 Recall@5 on Victorian Crime Diaries benchmark, outperforming Vanilla RAG, Temporal RAG, and GraphRAG variants. Cross-model evaluation across GPT-OSS 20B to TinyLlama 1.1B shows robust retrieval coverage at smaller scales.
Study of delayed generalization (grokking) during language model pre-training. Using an exposure-based framework and BLiMP minimal pairs, authors observe delayed generalization across five grammatical phenomena. Grammatical concept vectors become more predictive after generalization and occupy higher-dimensional subspaces.
FoLoRA, a forgetting-aware optimization framework, adapts foundation models via generalized Rayleigh quotient. It balances target task performance and preservation of pretraining capabilities by penalizing adaptation directions with low utility relative to forgetting. Tested on math, code, and instruction-following adaptation.
OPCD method to improve large models using weak critics. Instead of weak supervisors as labelers, they guide revisions. Progressive on-policy critique distillation filters high-quality critiques and distills critic-guided behavior into strong models via adaptive self-teacher signals. Results on reasoning and alignment benchmarks.
Study of LLM agent faithfulness in a Texas Poker simulator. Researchers measure the gap between stated reasoning and actual actions by decomposing the problem into two steps: reasoning-conclusion and conclusion-action. The two steps exhibit opposite behaviors.
Study combining cognitive-linguistic features (first-person pronouns, absolutist words, negative emotions) with DistilBERT embeddings and Holographic Reduced Representation to detect depression in Reddit posts. Hybrid model achieves F1=0.94 vs 0.80 TF-IDF baseline, AUC=0.981.
New arXiv paper proposing discrete-time approximations to SG(L)D with and without momentum, enabling accurate predictions of stationary covariance and integrated autocorrelation time. Non-asymptotic error bounds for practical tuning and uncertainty quantification, validated on misspecified models and large batch sizes.
Novel spatio-temporal graph anomaly detection method modeling spectral energy shifts. Detects camouflaged anomalies (decreased spectral variation) missed by existing spectral approaches. Energy-aware framework compatible with message passing, no specialized sequence modules required.
InfoAtlas is a foundation model that estimates mutual information between high-dimensional variables in a single forward pass, eliminating costly iterative optimization. Pretrained on synthetic data with diverse dependence patterns, it achieves 100× speedup over neural MI estimators while matching accuracy and generalizes to complex real-world scenarios.
Asymptotic theory for SGLD-Gibbs combining stochastic gradient Langevin dynamics with Gibbs updates for Bayesian inference in latent variable models. Authors derive jump-diffusion limits and propose hyperparameter tuning guidance ensuring statistically meaningful uncertainty quantification.
AEyeDE introduces an attention-based attribution framework for detecting AI-generated text using attention matrices from a proxy Transformer model. A lightweight CNN learns discriminative representations from these attribution maps. The method outperforms text-only baselines, shows strong generator-specific detection, and demonstrates robustness under cross-dataset transfer and spelling perturbations.
Method to evaluate collections of bivariate causal statements without access to ground truth. Authors introduce a compatibility score quantifying plausibility of induced causal models, without relying on faithfulness assumption. Applied to assessing causal claims from large language models.
CHAM-net, a contrastive hierarchical adaptive meta-network, predicts global methane flux by capturing site-specific dynamics through a conditioned encoder-decoder architecture. On simulation and observational datasets, the model achieves nRMSE=0.43 (emissions) and R²=0.97, outperforming all baselines.
VESTA is a statistical agent framework equipping VLMs with a dynamically growing exploration toolkit for quantitative model refinement. Evaluated on DAWN (benchmark for distribution fitting and time series modeling), VESTA outperforms prior agentic systems, especially on complex astronomy tasks (initial mass functions, gravitational-wave chirp signals).
CAST is an answer-free self-distillation method for GRPO (Group Relative Policy Optimization). It uses a stop-gradient self-teacher to shape token-level advantages by trajectory correctness, with bidirectional advantage sign reversal and bounded advantages for zero-variance groups. Improves mathematical reasoning performance.
Perturbative approach for nonparametric instrumental variable (NPIV) estimation inspired by perturbation theory in physics. Extends kernel ridge methods with systematic higher-order corrections. Reduces prediction error by up to 99% in high-dimensional ill-defined cases (β > 0.7) compared to standard ridge regression.
A legal framework for agentic AI tort liability. The paper proposes three interaction types (autonomous drift, pure tool use, collaborative planning) and uses interaction logs as primary evidence to determine where liability attaches. Introduces a "Reasonable Agent" standard based on constraint verification and forensic logging.
New quantization method preserving inner products with unseen vectors. Adaptive unbiased algorithms developed with theoretical guarantees. Practical implementations 2-10× faster than prior ASQ state-of-the-art while maintaining quality.
TRACE, a trajectory compression method, detects safety risks in long-horizon LLM agents. A Compressor encodes the full trajectory into a supervised latent evidence state, while a Reader judges safety using this reference. Achieves up to 12.6 percentage point improvements on ASSEBench, Pre-Ex-Bench, and R-Judge.
DOPA, a demonstration retrieval framework, uses an OOD proxy to approximate the inaccessible target domain and guide selection of relevant demonstrations. A Mahalanobis distance-based global diversity constraint ensures sufficient variety among retrieved examples. Positive results across multiple LLMs and tasks under severe distribution shift.
Humor generation system for SemEval-2026 Task-1 (MWAHAHA) using a "generate-many → select-best" strategy. Diverse candidate generation via multi-step prompting and model ensembling, then selection using a preference model trained on 2.5K human pairwise judgments. Ranked 1st in English and Chinese subtasks, 2nd in Spanish.
arXiv paper on temperature's role in LLM distillation. Authors show forward KL (FKL) outperforms reverse KL (RKL) at higher temperatures, contrary to prior empirical conclusions that omitted this parameter. Temperature enriches FKL with non-dominant token signals while merely rescaling RKL gradients.
ReMax formalizes exploration in RL through retries: a policy is evaluated by expected maximum return over M samples. Exploration emerges naturally without explicit bonuses. RePPO, a PPO variant optimizing ReMax, generalizes discrete M to continuous parameter m for fine-grained exploration control. Results on MinAtar and Craftax benchmarks.
GEM is a concept erasure framework for Rectified Flow Transformers. It bridges trajectory-based unlearning (Generative Flow Networks) and teacher-guided erasure, using geometric guidance signals to suppress unwanted concepts while preserving benign generation and preventing harmful content synthesis.
Masked diffusion models: a lightweight policy network learns optimal token unmasking order. Loss reweighting by denoiser probabilities. Outperforms heuristics on order-sensitive tasks like combinatorics and proteins.
Governance protocol for multi-agent knowledge bases combining formalized artifact lifecycle, reputation-weighted deliberative voting (Beta Reputation + EigenTrust), and graduated sanctions. Evaluation on 100 agents, 7 behavioral archetypes: 0.826 vs 0.791 precision under moderate adversity (p<0.001). Commit-reveal vote concealment yields +8.2-8.6pp improvement.
New automated metric (Triangulated Preference Shift score) to measure lexical biases introduced during preference learning (RLHF) in LLMs without manual curation. Analysis across 6 model families reveals a shift toward a 'language of prestige' (overuse of 'delve', 'furthermore').
Academic paper proposing product-aware autoencoders for anomaly detection in multi-product cyber-physical systems. Traditional global models create blind spots where attacks can evade detection. Tests on Tennessee Eastman Process benchmark: product-aware model achieves 100% detection accuracy versus 22.2% for global baseline in attack scenarios.
InfoNCE, the standard contrastive learning objective, relies on a statistical assumption misaligned with modern normalized embeddings. Authors propose WEINCE, a simple modification using extreme value theory and anchor-wise online batch statistics to correct softmax logits without additional parameters. Consistent improvements across 5 vision benchmarks.
Empirical study adapting Phi Silica for short-form text rewriting via dataset curation, prompt distillation, and parameter-efficient fine-tuning. Dataset built from public slide decks. Results: improved semantic fidelity, reduced hallucinations, higher preference win rate vs GPT-5-chat.
LaSR proposes a training paradigm for Speech LLMs featuring latent reasoning aligned to acoustic feature regions of target words. Without explicit intermediate tokens, the method improves specialized vocabulary recognition on Fun-Audio-Chat. A new Spoken Darwin-Science corpus for academic terminology is introduced.
Study on adaptive data selection for wearable prediction across 3 modalities (heart rate, activity, EMA) in longitudinal dataset. AUROC gains up to 0.7 for low-baseline participants, strong inverse correlation (r=-0.67) between improvement and baseline performance. Limited benefit for strong baselines.
BOUTEF is a multilingual corpus from 2 countries (Algeria, Tunisia) covering fake news, authentic narratives, comments, and debunking. Includes MSA, Algerian/Tunisian dialects, Arabizi, French, English, and code-switching. Analysis shows fake news relies on emotionally charged narratives and sensational framing, while debunking adopts a factual, verification-oriented style.
Online, distribution-free framework for controlling Conditional Value-at-Risk (CVaR) in non-stationary and adversarial environments. Combines conformal tail risk control, online learning, and Rockafellar-Uryasev variational representation. Provable safety guarantees for nonlinear tail risk under arbitrary data-generating processes. Applications: portfolio risk management and LLM toxicity mitigation.
Intel launches Crescent Island at Computex 2026, a professional GPU based on Arc Xe 3P architecture with up to 480GB VRAM using LPDDR5X. 350W TDP, supports FP4/MXFP4 to FP64 formats for next-generation AI workloads.
A LightGBM quantile regression model ranked a Bayesian target-encoded feature #1 by importance for watch price forecasting, but 4-seed × 3-variant ablation showed +0.28pp MAPE regression on hold-out. The learned signal was irreducible label noise (unobserved factors), failing to generalize.
MLE-Bench shows 80% gains over two years, but new research (FML-Bench) reveals little comes from real algorithmic progress. At equal step budget and identical models, the two-year-old AIDE algorithm matches modern agent/evolutionary search systems. FML-Bench unifies code editing agents, step definitions, and val/test splits to benchmark algorithmic efficiency.