2731 articles
Nvidia releases a task-seeded synthetic Q&A generation method for Nemotron pretraining. The technique uses task seeds to create diverse, high-quality training data, improving model performance across varied benchmarks.
Amazon deploys an AI generative image function to facilitate purchases. The capability allows users to visually generate products from text descriptions, integrating image generation directly into the shopping journey.
Accenture invests in AlphaSense to strengthen its agentic AI strategy for enterprises. AlphaSense is an influential platform in the artificial intelligence market.
Caveman is a Claude Code skill that reduces token consumption by 65% using minimalist language style. The project leverages models' ability to understand ultra-condensed instructions.
LeanCTX is a context OS for AI development. Single local binary compresses, remembers, routes, and verifies tokens between code and model. 63 MCP tools, 10 read modes, up to 99% token savings. Works with Cursor, Claude Code, Copilot, Windsurf, Gemini.
Sam Altman, Dario Amodei, and Demis Hassabis urge the US Congress to mandate screening of synthetic DNA orders. AI systems already outperform PhD-level virologists on lab procedures, raising risks of misuse for biological weapons.
A Gemma team member (Omar) confirms on Reddit that Gemma 4 QAT will release soon. Users are advised to wait for this refined version rather than testing current quantizations.
Benchmark raises $2 billion for its first growth fund, marking a new chapter for the venture capital firm.
ChatGPT introduces a memory system to retain user preferences and context across conversations, making the assistant more relevant and helpful.
Study shows 60% of UK media articles on AI fail to disclose authors' defence sector connections. The lack of transparency raises concerns about conflicts of interest in coverage of military AI applications.
GitHub repository with implementations of various Transformer attention mechanisms. Originally developed for Small Language Model experiments and benchmarking, applicable to Computer Vision, Vision Encoders, RL, and other domains. Open to community contributions.
Meta launches Business Agent, an AI agent to automate sales and customer support on its platforms. The tool processes over one billion daily conversations between businesses and consumers.
xAI releases grok-imagine-video-1.5-preview, an image-to-video model generating cinematic videos up to 720p from still images and text prompts. Multiple clips can be stitched together into longer scenes.
On Jetson AGX Orin 64GB, q8_0 quantization delivers 20% faster prefill than q6_k and 10% faster than q4_k_xl. Tested with Qwen 3.6-27B-MTP-GGUF on recent llama.cpp: q8_0 reaches 245 tokens/s vs 190 for q6_k. EMC not saturated, suggesting CUDA optimization issue rather than memory bandwidth constraint.
Google launches Dreambeans, an AI app generating personalized micro-stories based on user data. The app offers an alternative to endless scrolling on traditional social networks.
Turner and Townsend benchmarks traditional data centers at $11.3M/MW versus $4.5-6.5M/MW for factory-built modular configurations. Key advantage: 90-120 day deployment timeline versus 18-24 months, reducing revenue gap by 12-18 months on $50M projects. Module joint failures under high-density load are manageable with ISO 668 specs and liquid cooling for racks above 50kW.
DeepSeek is reportedly preparing a $7 billion funding round with Tencent and CATL, which would be one of the largest recent AI funding rounds in China.
Miso Labs releases Miso One, an open source text-to-speech model positioned as next-generation technology. The article lacks technical details, performance metrics, or benchmarks against existing solutions.
UK's CMA imposes technical opt-out for Google's AI Overviews without establishing publisher compensation rights. This framework differs from contractual licensing based on neighboring rights.
Nvidia Nemotron 3 Ultra, an open Mixture-of-Experts model with 1M token context window, is now available on Vercel AI Gateway. Optimized for multi-turn agent workflows (planning, tool use, delegation), it reaches 350 tokens/sec with 30% cost reduction on agentic tasks. Integration via SDK with Zero Data Retention support.
Community thread on Gemma 4 12B: users share hardware setups, quantizations (GGUF, MLX, BF16) and actual performance metrics (tokens/sec, context length). Discussion on 16GB laptop feasibility vs 32GB+, MLX vs GGUF speed on Apple Silicon, real-world multimodal usage.
User reports MTP (Multi-Token Prediction) provides no performance improvement for Qwen3.6-35B GGUF on RTX 5060Ti: ~60 tok/s in both cases. Tests with unsloth flags (spec-type draft-mtp, spec-draft-n-max 2) but observes no speedup despite reducing ctx-size and quantization.
Gemma 4 12B heretic model tested for code generation: single-prompt retro game creation (45k tokens total). Consistent 18.44-18.93 t/s throughput, 4,372-token code generation in 4 minutes. 91.7-96.4% cache reuse on llama.cpp with Ryzen 9 9950X + RX 6800.
BiasGRPO introduces Group Relative Policy Optimization (GRPO) to mitigate social bias in LLMs. By normalizing rewards across sampled completions, the method stabilizes training compared to DPO and PPO. Authors release a compute-efficient bias reward model and extended dataset for multi-objective RLHF.
AIP (Agent Instruction Protocol) models agent skills as directed execution graphs with deterministic nodes and typed edges validated by YAML schema. On 27 SkillsBench tasks, Claude Sonnet improves from 0.60 to 0.71 mean reward and 53% to 67% pass rate. The graph structure enables precise failure diagnosis and iterative skill improvement.
Position paper arguing deployed RL systems should adopt continual learning instead of train-then-fix paradigm. Authors identify four sources of post-deployment non-stationarity requiring never-ending learning and analyze real-world continual RL examples.
Researchers demonstrate that aligned LLMs remain vulnerable to token injections at any generation step, not just early tokens. Alignment with internal refusal directions does not predict robustness. Training directly on perturbed generation trajectories improves resistance to mid-sequence attacks.
FALSIFYBENCH evaluates inductive reasoning in 12 LLMs through rule discovery games inspired by the Wason 2-4-6 task. Reasoning models outperform instruction-tuned models, but none approach optimal performance. Success depends primarily on capacity for negative testing and hypothesis falsification.
Systematic study of spectral behavior in Muon, an orthonormalization-based optimizer using Newton-Schulz iteration. Across 77M-2.8B parameter models, singular value quantiles of momentum buffers stabilize following power laws dependent on layer depth (exponents M^-0.25 to M^-0.96). Implications for NS configuration at frontier scale.
SANE introduces a schema-aware text-to-SQL evaluation paradigm for domain-specific biological data, using automatically generated benchmarks tied to real experimental structures. Few-shot LLMs achieve accurate query generation without fine-tuning when combined with schema-aware prompting; failures stem mainly from ambiguous inputs rather than SQL generation errors.
Researchers demonstrate a jailbreak family using fanfiction subgenres (Archive of Our Own) as universal attack carriers against 8 aligned LLMs. Requiring no attacker LLM or per-target adaptation, the method raises mean ASR from 0.278 to 0.731. Four-turn extension SAGA-A4 achieves 0.924 ASR.
SGR integrates LLMs with external knowledge graphs via schema-guided subgraph generation. Framework extracts entities and relations, retrieves compact subgraphs, then combines direct Cypher reasoning and multi-path validation. Experiments on CWQ, WebQSP, GrailQA, KQA Pro show improved accuracy and Hits@1 over baselines.
SCORE, a co-evolutionary framework, couples an evaluator and generator in a shared-parameter learning process to improve deep research report generation. A meta-harness dynamically controls the evaluation environment based on solver performance, avoiding optimization saturation seen with static evaluators.
Systematic study of QKV variants in transformers. Authors test three projection sharing constraints (Q-K=V, Q=K-V, Q=K=V) on synthetic tasks, vision, and language models (300M-1.2B parameters). Q-K=V reduces KV cache by 50% with 3.1% perplexity degradation. Combined with GQA/MQA, achieves 87.5-96.9% cache reduction for edge inference.
Dynamic Infilling Anchors (DIA) is a training-free method for diffusion large language models (dLLMs) that dynamically adjusts anchor positions to generate structured content (JSON, reasoning templates). Tested on GSM8K and MATH, DIA improves format compliance and answer accuracy by avoiding rigid spans.
CAPR is a reinforcement learning algorithm for diffusion language models that leverages the denoising trace to generate fine-grained supervision signals without full tree expansion cost. The approach reduces rollout cost to 0.75x flat methods and 0.6x tree methods, achieving SOTA on 4x4 Sudoku, Countdown, GSM8K, and Math500.
Boolean Task Algebra (BTA) enables zero-shot task composition in RL. Authors prove that in deterministic MDPs, optimal Q-value functions collapse to universal and empty tasks, making logarithmic base tasks redundant. They propose a goal-set-based composition method reducing learning and composition costs while maintaining policy performance across tabular, visual, and continuous-control domains.
dMX is a differentiable mixed-precision quantization framework for learnable floating-point bit-width assignment across LLM layers. Tested on Llama, Qwen3, and SmolLM2 using the MXFP standard (Open Compute Project), it optimizes layer formats continuously then discretizes via annealing, outperforming KL-divergence heuristics on WikiText-2 and zero-shot reasoning benchmarks.
VRPCoder, an 8B model, translates natural-language vehicle routing scenarios into Gurobi code. Authors propose constraint injection to verify constraints are neither silently omitted nor spuriously added. With GRPO, VRPCoder reaches 93% Pass@1, outperforming Claude-Sonnet-4.5 by 28 points on VRP benchmarks.
The study shows that edge of stability (EoS) selectively redistributes learning across data subgroups. Two conditions enable a group to benefit: alignment of its aggregate gradient with the top Hessian eigenvector, and sustained non-vanishing gradient magnitude. Under cross-entropy loss, gradient saturation favors output-outliers while suppressing progress on confidently classified groups.
Cartridges at Scale (CAS) trains modular, reusable KV caches for large document collections, eliminating costly prefilling. The framework dynamically manages hundreds of per-document cartridges with GPU/storage rotation, scaling to over one million tokens. Performance: +10-31 points vs monolithic cartridge, 3-4x fewer tokens than conventional RAG.
VAMPS is a benchmark of 1,168 bilingual multimodal questions testing whether multimodal LLMs can solve mathematical problems by constructing and reasoning over graphs. Results show direct analytical solving outperforms tool-enabled visual solving across models, even on problems where plotting is the natural strategy.
SMAC-Talk extends StarCraft Multi-Agent Challenge with natural language communication to evaluate LLM-based agents in cooperative multi-agent environments. Open-source benchmark testing decentralized control, partial observability and long-horizon decision-making, including scenarios with deceptive communicators. Evaluation on Qwen3.5 models.
PEEL is a methodological framework combining deterministic analysis (Voyant Tools) and Claude interpretation to assess LLM epistemic accountability in research. Applied to AI-generated summaries, it reveals systematic distortions invisible without non-AI measurement and proposes three implications: instrument AI tools, distinguish fluency from fidelity, design epistemic authority.
RUBAS is a rubric-based reinforcement learning framework for LLM agent alignment. It decomposes behavior into four dimensions (tool-use safety, argument safety, response safety, helpfulness) and generates structured rewards over complete trajectories. Experiments show improved safety and reduced hallucinations while maintaining utility.
Theoretical and empirical study challenges adaptive patching effectiveness for time-series Transformers. Authors show well-tuned uniform patch allocation rivals dynamic approaches on standard benchmarks, and local complexity alone does not justify non-uniform patching under common forecasting losses.
AXON is a training-free module improving parallel decoding for discrete diffusion language models. It selects anchor tokens (confident masked tokens) that uncertain positions attend to via attention signals, reducing denoising steps while maintaining quality on reasoning and code-generation benchmarks.
LDARNet, a 120M-parameter genomic foundation model, introduces learnable adaptive tokenization without supervision to replace fixed schemes (k-mers, BPE). Combining BiMamba-2 with local attention and bidirectional routing, it achieves state-of-the-art on 5 histone modification tasks and outperforms models 20× larger at matched compute.
IEEE P3109 draft standard defines parameterized binary floating-point formats for machine learning with variable precision, stochastic rounding, and saturation modes. Operations are exception-free and formally verified.
Pseudospectral theory for coupled gradient descent (bilevel optimization, two-time-scale stochastic approximation, adversarial training). Kreiss constant bound K(J) ≤ 2/(1-γ) + ‖C‖/(4(1-γ)) for block-triangular Jacobians. Finite-horizon iteration complexity O(K(J)² log(1/δ)). Experiments on linear-quadratic problems and neural network training.
New FFR approach retrieves coherent multi-utterance, multi-image fragments from long-form multimodal dialogues. Two models: F2RVLM (generation + RL with multi-objective rewards) for single-dialogue, FFRS (two-stage indexing + retrieval) for corpus-scale. MLDR dataset introduced, superior performance on benchmarks.
Pre-deployment verification framework for enterprise AI agents combining operational envelope, ontology-grounded scenario generation, and trust certificate. Pilot across 4 regulated sectors (Fintech, Banking, Insurance, Healthcare) generating 1,800 scenarios: 48.3% regulatory coverage vs 33.1% persona baseline (p=0.0006). Cross-validation on Claude Sonnet 4, Qwen 2.5 72B, Gemma 4 26B.
Curation-Bench evaluates whether generalist AI agents can automate training data curation. Agents reach published baselines within ten iterations but tend toward local policy variants. With scaffolding requiring method citation and adaptation, an agent autonomously composes a data-selection policy outperforming strong baselines at one-tenth their data budget.
SGDR, an online skill learning method, enables web agents to reuse sub-procedures at each execution step. Unlike static approaches, SGDR dynamically retrieves skills based on current webpage state and task goal. On WebArena, it achieves 37.5% success with GPT-4.1 and 24.3% with Qwen3-4B, outperforming strongest baselines by 10.6% and 10.0% respectively.
EvalStop detects and stops RLHF jobs that overoptimize the reward model at the expense of real-world metrics. On 80% RLHF workloads (64 GPUs), the system achieves 98% precision and cuts wasted compute by 22% while improving JCT by 9% over SRTF-Est.
Trivium proposes optimizing three regret objectives for agentic systems: outcome regret, epistemic regret, and temporal regret. Temporal regret captures how long miscalibrated causal models persist. On CausalBench-Seq, Trivium achieves O(log E) temporal regret versus linear growth for outcome-only baselines.
MechSim is a neuro-symbolic reasoning framework enabling LLM agents to reason about mechanisms, assumptions, and execution behavior of scientific simulators. It represents simulators through structured schemas capturing assumptions, variables, and mechanism dependencies, generating evidence-grounded explanations linking simulator outcomes to underlying mechanisms rather than treating them as black boxes.
Theoretical paper on exact unlearning in reinforcement learning. Authors propose a ρ-TV-stable RL algorithm enabling user data deletion with computational cost only ρ√ln T fraction of retraining. Regret bound O(H²√SAT + H³S²A + H^2.5S²A/ρ) for tabular MDPs, with nearly minimax-optimal lower bound.
Study of scaling rules for Gated Delta Networks via μP. Authors derive optimal parametrizations for learning-rate transfer across model widths. Experimental validation on LLM pre-training with AdamW and SGD.
Systematic study of positional bias in multi-video summarization with MLLMs. Benchmark on ActivityNet and News videos (2-4 inputs). Evaluation of 9 models (open-source and proprietary) using three metrics: Coverage, Directional Positional Bias, Middle-Edge Gap. Finding: positional effects are domain- and model-dependent; increasing visual budget does not uniformly remove imbalance.
Recover-LoRA extends a data-free accuracy recovery method to 2-bit quantized LLMs. A mixed-precision strategy selectively quantizes MLP gate/up layers to W2 while keeping others at W4, achieving 7.5–23.3% throughput gains. Low-rank adapters trained via logit distillation on synthetic data recover 80–95% accuracy on Qwen3-4B using only 10k samples.
Study on affinity-based reinforcement learning to instill virtuous behavior in AI agents. Researchers test this technique in Fog of Love, a complex multi-agent environment where two agents must balance individual competition and relational cooperation. Localized affinities improve performance and make agent behavior interpretable.
Authors propose a framework for multi-agent systems where disagreement between agents is treated as an informative signal rather than error to eliminate. They define four disagreement states based on reasoning-trace similarity and conclusion agreement, applied to content moderation with defeasible strategic routing.
ACAT is a web-based collaborative platform for Aspect-Based Sentiment Analysis (ABSA) dataset annotation. It supports 4 ABSA workflows, automates ETL and computes Inter-Annotator Agreement metrics. On 1,002 restaurant reviews, median annotation time 31.58s, IAA 0.78–0.86.
Large-scale empirical study of 284 linguistic features across 27 LLMs and 10 text domains for detecting AI-generated text. Classifiers based on linguistic features reliably distinguish AI from human text. Lexical richness remains robust across model families and domains, while other indicators prove strongly context-dependent.
arXiv paper showing RL applied directly to pre-training checkpoints (without prior SFT) is effective from early stages. Pre-training data composition impacts RL effectiveness more than model scale. Merging RL and SFT objectives via parallel averaging outperforms standard pipelines while preserving general capabilities.
Longitudinal study showing that daily 5-minute conversations with AI over 28 days decreased preference for human support by 10.3% and increased AI preference by 11.6%. Emotional support emerges incidentally within routine interactions, not just dedicated chatbots, progressively redirecting relational choices.
ADAPTOOD is a framework for adapting ECG time series models to distribution shifts. It uses data uncertainty to quantify shift severity and guides fine-tuning with low-rank updates and adaptive hyperparameter optimization. Results: +7% accuracy and +12.9% precision on OOD tasks.
Diagnostic study on offline model selectors for edX dropout prediction. Authors identify three failure causes (mismatched learner, non-predictive state, label shift) via three stages: oracle ceiling via k-NN, BC/DQN/CQL evaluation, state ablation. Across 5 models, oracle gains 9.7 accuracy points, but learners plateau due to local representational ambiguity.
BiNSGPS establishes bidirectional interaction between a multimodal LLM adviser and a symbolic solver for geometry problem solving. The system dynamically rectifies inconsistent formal representations and proposes auxiliary hypotheses based on solver feedback, eliminating early-stage errors from unidirectional neuro-symbolic pipelines.
Tree-based formalization of complementarity in multi-agent human-AI interactions. Framework models HAI protocols as rooted planar binary trees with prediction vectors at leaves. Key results: complementarity unattainable via selector-based HAIs, achievable in regression (closed form for N=2), obstructed in classification under endpoint-monotone losses.
A PIML framework incorporates hydrological constraints into an LSTM loss function for flood prediction. The Trend Alignment constraint penalizes inconsistencies between precipitation and discharge trends. On limited data (5%), the model achieves NSE=0.23 vs 0.20 for standard LSTM, with improved stability under extreme conditions.
SARAF, a retrieval-augmented time series forecasting framework, adaptively balances relevance and diversity based on dataset stationarity. It selects heterogeneous historical segments and aggregates their futures with stationarity-aware fusion. Experiments on 8 real-world datasets show improved accuracy and robustness over baselines.
XGBoost model for three-class Alzheimer's detection (normal cognition, mild cognitive impairment, Alzheimer's) on 1,641 ADNI subjects. Macro AUC-ROC 0.983 in cross-validation, 0.982 on test set. SHAP analysis identifies CDR Global as dominant predictor for NC/MCI, CDR-SB and MMSE for AD.
R-APS improves LLM reliability in agentic settings via reasoning-mode decomposition. Tested on planar mechanism synthesis, it delivers robustness certificates 3.5× tighter than baselines, 46% faster iterations-to-first-admission, and 2.1× Chamfer-distance reduction. No fine-tuning required; operates via structured protocol on frozen LLM.
MIRAGE is a framework for mobile agents that learns continuous latent reasoning representations from visible textual reasoning traces. It transfers explicit reasoning into compact hidden states, reducing token generation by 3-5x on AndroidWorld while matching performance and improving the baseline by 10.2 points.
Survey paper positioning LLMs within the longer history of computational approaches to concept analysis in history, philosophy, and sociology of science. Examines what LLMs add to prior methods (early digital methods, distributional approaches, lexical semantic change detection) and persistent methodological challenges: corpus construction, operationalization, evaluation and interpretation.
Variance reduction method for heavy-tailed monetization metrics in ranking experiments. Combines post-stratification with CUPED on pre-experiment covariates. Deployed at ShareChat: reduces variance by ~45% without additional traffic.
Foundation models based on physical principles (Fourier decomposition, energy conservation, symmetry) rather than statistical correlations. Trained on RF data, a 1.99M parameter frozen encoder achieves 77.7% average accuracy across 15 diverse tasks via linear probing without fine-tuning. Superior performance on physically-grounded tasks (84.5%) versus semantic tasks (70.0%).
Parthenon is a self-evolving legal-agent framework tested on 12,510 trajectories. It decomposes the system into roles (model, harness, agent, knowledge, tools, skills) for traceability and compliance. A leak-free learning loop converts failures into improvements to skills and tools without modifying model weights.
Metric-aware hybrid approach for CTF4Science Lorenz challenge mixing short-horizon forecasting, long-time distribution matching, and trajectory reconstruction. Multi-model system: synthetic-pretrained denoisers, Lorenz ODE fitting for first 20 steps, histogram-tail substitution with synthetic libraries. Public leaderboard score 83.84.
SKMD (Stein Kernelized Molecular Dynamics) is an enhanced sampling method for active learning of ML-based interatomic potentials. It uses interacting particle dynamics and preserves the Boltzmann distribution, balancing exploration with convergence to high-probability energy landscape regions. Demonstrated on Müller-Brown potential and MACE potential for alanine dipeptide.
Tabular reinforcement learning for metro network expansion (MNEP). Reformulated as Non-Markovian Rewards Decision Process (NMRDP): matches Deep RL performance with 18× fewer training episodes and 12× lower carbon emissions. Incorporates social equity criteria. Validated on Xi'an and Amsterdam.
SCI-PRM is a process reward model trained on SCIPRM70K, a 70K-trajectory dataset of scientific reasoning interleaved with tool execution. It supervises tool selection, execution accuracy, and result interpretation. Tested on biology, chemistry, physics: improves test-time scaling and provides dense reward signal for RL.
KODA is a kernel-based framework for comparing and aligning vision-language model representations (CLIP, SigLIP). The method identifies sample subsets weakly clustered under one representation but strongly clustered under another through constrained optimization and low-rank approximations. Code released.
Theoretical paper defining Bayes-sufficient representations in supervised learning. A representation is Bayes-sufficient if a prediction head can use it to implement a Bayes-optimal action rule. The framework connects sufficiency to property elicitation and shows that loss determines the relevant information required.
Researchers show LLMs trained with reinforcement learning exploit gaps in societal rules like they hack reward functions. Using SocioHack (72 societal environments), they demonstrate models discover regulatory loopholes that remain technically compliant while defeating intent. Current safeguards provide limited mitigation.
SaliMory is a training framework for conversational agents with persistent memory. It uses hierarchical cognitive structure (user facts, preferences, working memory) and stage-wise decomposed rewards to supervise memory operations (filtering, consolidation, cue-driven recall). Results: -33% memory-attributed failures, +10% end-to-end accuracy, +100% Good Personalization rate.
GlossAssist is an automated glossing tool for linguistic documentation built on CWoMP (Contrastive Word-Morpheme Pre-training). It integrates active learning: each annotator correction enriches a mutable lexicon of morpheme representations without model retraining. The interface enables field linguists to incorporate expertise directly into model behavior.
Large-scale study across 5 models (7B-72B parameters), 10 biomedical QA datasets, and 4 retrieval methods. RAG yields only +1-2 point improvements over no-retrieval baseline. Backbone model choice has larger impact than retriever or corpus selection. Bottleneck: model's limited ability to effectively use retrieved evidence.
Researchers demonstrate that steering vectors applied during inference can bypass refusal mechanisms in Mixture-of-Experts (MoE) LLMs. Two expert-aware methods exploit refusal-specific routing patterns and expert-specific steering directions to suppress refusal behavior. Results suggest attention plays a substantial role in MoE refusal behavior alongside expert routing.
LazyAttention is an attention mechanism optimizing key-value caching for RAG and in-context learning by deferring positional encoding. It enables zero-copy KV reuse at arbitrary positions, reducing time-to-first-token by 1.37× and increasing throughput by 1.40× versus Block-Attention.
Geometric study of counterfactual explanations in modern ML systems. Researchers show that models with similar predictive performance can differ substantially in the feasibility and distance of counterfactual changes, determined by decision-boundary proximity and local data support.
CausalBERT-based methodology to isolate the impact of each aspect on overall online review ratings. Enhancements: temperature scaling, hyperparameter optimization, interpretability methods. Validation on 600K+ reviews of U.S. schools: school administration and benchmark performance are key drivers.
SePO (Self-Evolving Prompt Optimization) optimizes agent system prompts via self-referential evolutionary search. The prompt agent improves both task agents' prompts and its own prompt. Two-stage training: multi-task pre-training + target task fine-tuning. Average accuracy gain of 4.49 points vs Manual-CoT across AIME'25, ARC-AGI-1, GPQA, MBPP, Sudoku.
Training-free lexical-dense fusion study for long-term conversational memory retrieval. Score-level fusion of late-interaction dense + BM25 improves Hit@1 by +8.8 to +17.2 points across six encoders (Hit@1 0.752 with e5-large-v2). Web search cross-encoder reranker degrades results (-6.9 pp). Analysis shows division of labor: dense excels on multi-hop/temporal questions, BM25 on adversarial ones.
MONIR is a Modalized-Output Normative Intermediate Representation for ASP-based compliance reasoning. The framework features staged operational semantics, executable compilation (MONIR-ASP), and extensions for external functions and temporal rules. Validated on Chinese ADAS regulations using an LLM-assisted pipeline.
Fine-tuned RoBERTa outperforms zero-shot LLMs (Claude Haiku, Gemini Flash) on Reddit misinformation response classification: 0.62 macro-F1 vs 0.50. Model scaling (Llama-3-70B vs 8B) provides no benefit. Safety alignment in commercial models degrades belief detection (0.17 for Claude Sonnet).
DMAIC-IAD is a multi-agent system inspired by the DMAIC quality-management framework for industrial anomaly detection. It plans strategies first via standardized operating procedures, then uses a pre-trained judge model to evaluate candidates without costly execution. 37.76% improvement over existing agentic baselines.
Novel framework for learning admissible heuristics in optimal planning via cost partitioning. A neural network with axial self-attention predicts cost weights guaranteed admissible by construction, leveraging Lagrangian dual equivalence. Results show reduced node expansions versus suboptimal baselines while preserving optimality.