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The full Modelwire feed, ordered by publish time.

Illustration for: Less Back-and-Forth: A Comparative Study of Structured Prompting

Less Back-and-Forth: A Comparative Study of Structured Prompting

Structured prompt design substantially outperforms unguided prompting across multiple LLM systems and task domains, with checklist-based approaches yielding 32% higher quality scores than raw prompts. This empirical finding addresses a core friction point in LLM deployment: the gap between model capability and user ability to extract it. The research validates that prompt engineering is not merely a user-skill problem but a systematic design challenge, with implications for how organizations should architect AI workflows and whether future systems should embed structured input mechanisms by default.

arXiv cs.CL·May 19

58

Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization

Researchers address a critical failure mode in Bayesian optimization: Gaussian process models often misestimate uncertainty in the lower tail of predictive distributions, directly degrading the quality of expensive black-box function evaluations. This work introduces goal-oriented calibration techniques that align GP confidence estimates with actual performance below a target threshold, improving the exploration-exploitation balance in settings where every evaluation carries high cost. The fix matters for practitioners tuning hyperparameters in deep learning, materials discovery, and other domains where BO drives resource allocation.

arXiv cs.LG·May 19

52

Illustration for: TrajTok: Adaptive Spatial Tokenization for Trajectory Representation Learning

Research Models & Releases

TrajTok: Adaptive Spatial Tokenization for Trajectory Representation Learning

TrajTok addresses a persistent challenge in mobility AI: converting noisy, irregularly sampled GPS traces into learnable representations without losing spatial nuance. The core innovation is adaptive hexagonal tokenization that avoids the false choice between sparse fine grids and lossy coarse ones. By combining multi-resolution spatial partitioning with a factorized transformer that separates geometric and kinematic reasoning before fusion, the work enables pretraining of transferable trajectory encoders. This matters for downstream tasks in urban computing, autonomous systems, and location intelligence where pretrained embeddings could reduce annotation burden and improve generalization across geographies.

arXiv cs.LG·May 19

58

Tools & Code Research

FiLark: a streaming-first software framework for end-to-end exploration, annotation, and algorithm integration in distributed acoustic sensing

FiLark introduces a streaming-first architecture for processing distributed acoustic sensing data, addressing a critical gap in real-time ML workflows. Traditional batch-oriented frameworks struggle with DAS systems' ultra-high-channel-count continuous streams, forcing practitioners into manual segmentation and offline analysis. This framework unifies data access, signal processing, visualization, and monitoring under a streaming paradigm, enabling interactive exploration and algorithm-in-the-loop validation at scale. The work signals growing infrastructure maturity around continuous sensor data pipelines, a constraint that affects time-series ML, edge deployment, and scientific computing broadly.

arXiv cs.LG·May 19

54

Illustration for: MixRea: Benchmarking Explicit-Implicit Reasoning in Large Language Models

Research Models & Releases

MixRea: Benchmarking Explicit-Implicit Reasoning in Large Language Models

Researchers have identified a critical failure mode in advanced LLMs: the tendency to miss subtle contextual signals when explicit instructions dominate, mirroring human inattentional blindness. The MixRea benchmark, spanning 2,246 questions across nine reasoning types, exposes a consistency gap even in frontier models like Gemini 2.5 Pro, which achieves only 42.8% accuracy on mixed explicit-implicit tasks. This finding matters for deployment in high-stakes domains where overlooked nuance can compound into costly errors, signaling that current scaling and instruction-tuning approaches may not fully address reasoning robustness.

arXiv cs.CL·May 19

62

Illustration for: Beyond Prediction Accuracy: Target-Space Recovery Profiles for Evaluating Model-Brain Alignment

Beyond Prediction Accuracy: Target-Space Recovery Profiles for Evaluating Model-Brain Alignment

Researchers propose a new evaluation framework that moves beyond raw prediction accuracy to assess which specific dimensions of brain activity vision models actually capture. By analyzing reproducible fMRI response patterns across subjects, the work reveals that high prediction scores can mask incomplete recovery of the target brain's representational structure. This matters for neuroscience-grounded AI development: it reframes model-brain alignment as a question of dimensional fidelity rather than correlation strength, potentially exposing gaps in how well current vision architectures map onto biological visual processing.

arXiv cs.LG·May 19

58

Illustration for: Optimizing Computational-Statistical Runtime for Wasserstein Distance Estimation

Research Tools & Code

Optimizing Computational-Statistical Runtime for Wasserstein Distance Estimation

Researchers tackle a foundational computational bottleneck in optimal transport theory, proposing methods to estimate Wasserstein distance between probability distributions with subquadratic runtime scaling. The work bridges statistical sampling and algorithmic efficiency, directly relevant to practitioners building generative models, domain adaptation systems, and distribution-matching pipelines where Wasserstein metrics serve as loss functions or evaluation criteria. Faster, provably accurate distance estimation reduces training time and enables larger-scale experiments across machine learning workflows that depend on measuring distributional divergence.

arXiv cs.LG·May 19

58

Illustration for: Toto 2.0: Time Series Forecasting Enters the Scaling Era

Models & Releases Research

Toto 2.0: Time Series Forecasting Enters the Scaling Era

Toto 2.0 demonstrates that time series forecasting models exhibit reliable scaling properties across a 600x parameter range, from 4M to 2.5B weights. The release of five open-weight checkpoints trained under a unified recipe marks a shift toward foundation model approaches in a traditionally fragmented forecasting domain. State-of-the-art results on three benchmarks, including a contamination-resistant evaluation, signal that scaling laws observed in language models may generalize to temporal prediction tasks. The open Apache 2.0 release and detailed hyperparameter transfer methodology lower barriers for downstream practitioners, potentially accelerating adoption of large forecasting models in observability and financial applications.

arXiv cs.LG·May 19

68

Research Tools & Code

k-Inductive Neural Barrier Certificates for Unknown Nonlinear Dynamics

Researchers have extended barrier certificate theory, a formal verification technique, to handle nonlinear systems with incomplete knowledge by relaxing strict safety constraints through k-inductive relaxation. The approach combines neural networks for scalability with counterexample-guided synthesis and SMT solvers to guarantee safety properties without full system models. This bridges a critical gap in autonomous systems verification where practitioners often lack complete dynamics information, making formal guarantees tractable for real-world control problems where conventional methods fail.

arXiv cs.LG·May 19

52

Illustration for: Beyond Isotropy in JEPAs: Hamiltonian Geometry and Symplectic Prediction

Beyond Isotropy in JEPAs: Hamiltonian Geometry and Symplectic Prediction

Joint-Embedding Predictive Architectures (JEPAs) commonly enforce isotropic Gaussian constraints on single-view representations, a design choice that embeds Euclidean symmetry by default. New theoretical work reveals this assumption carries measurable cost: when downstream tasks require structured geometry, isotropy provably misaligns the learned representation, and no fixed marginal target remains optimal across all possible task geometries. Critically, even perfect knowledge of single-view statistics cannot recover the cross-view predictive structure JEPAs aim to learn. These findings challenge a foundational regularization pattern in self-supervised learning and suggest representation learning frameworks should adapt their geometric priors to task structure rather than defaulting to symmetry.

arXiv cs.LG·May 19

58

Illustration for: Optimal Representation Size: High-Dimensional Analysis of Pretraining and Linear Probing

Optimal Representation Size: High-Dimensional Analysis of Pretraining and Linear Probing

Researchers have formalized the pretraining-then-probe paradigm as a tractable high-dimensional optimization problem, deriving closed-form expressions for how representation dimensionality, unlabelled sample size, and labelled data volume jointly determine generalization. This theoretical framework bridges the gap between empirical scaling laws and first-principles understanding of why modern two-stage training works, offering practitioners a principled way to set representation bottleneck sizes rather than relying on heuristic tuning. The result matters for anyone designing efficient transfer pipelines, from foundation model developers to edge deployment scenarios where compute is constrained.

arXiv cs.LG·May 19

58

Illustration for: Draft Less, Retrieve More: Hybrid Tree Construction for Speculative Decoding

Research Tools & Code

Draft Less, Retrieve More: Hybrid Tree Construction for Speculative Decoding

Speculative decoding has emerged as a key inference acceleration technique for LLMs, but existing approaches face a fundamental tradeoff: dense draft trees maximize token acceptance but consume prohibitive memory bandwidth, while pruning reduces overhead at the cost of discarding valid candidates. Graft resolves this tension by reframing pruning and retrieval as complementary operations, redirecting the computational budget freed by branch removal into candidate recovery. This work addresses a critical bottleneck in production LLM serving where inference latency directly impacts cost and user experience, making it relevant to anyone optimizing model deployment at scale.

arXiv cs.LG·May 19

62

Illustration for: INSHAPE: Instance-Level Shapelets for Interpretable Time-Series Classification

INSHAPE: Instance-Level Shapelets for Interpretable Time-Series Classification

INSHAPE addresses a structural gap in time-series classification by shifting from dataset-wide shapelet discovery to instance-specific pattern recognition. Traditional approaches optimize discriminative temporal motifs across entire populations, creating misalignment with individual data points and obscuring true decision drivers. This work treats shapelets as interdependent rather than isolated, capturing temporal interactions that prior methods ignore. The advance matters for practitioners building interpretable forecasting and anomaly detection systems where local pattern fidelity directly impacts both model accuracy and explainability credibility in regulated domains.

arXiv cs.LG·May 19

58

Illustration for: ThoughtTrace: Understanding User Thoughts in Real-World LLM Interactions

Research Tools & Code

ThoughtTrace: Understanding User Thoughts in Real-World LLM Interactions

ThoughtTrace addresses a fundamental gap in LLM evaluation: the absence of ground-truth user intent data. By pairing 17,000+ conversation turns with annotated user thoughts across 20 models, researchers have created a benchmark that exposes how poorly frontier LLMs infer human reasoning from context alone. This dataset reshapes how the field measures alignment and user satisfaction, moving beyond surface-level response quality to the cognitive and motivational layer that drives real interactions. For model developers and safety researchers, ThoughtTrace provides empirical evidence that understanding user intent remains a critical unsolved problem.

arXiv cs.CL·May 19

68

Illustration for: What Do Evolutionary Coding Agents Evolve?

Research Tools & Code

What Do Evolutionary Coding Agents Evolve?

A new dataset called EvoTrace exposes a blind spot in how evolutionary coding agents are evaluated. While LLM-plus-evolution systems have shown promise in algorithm design and mathematical discovery, researchers typically measure success only by final task scores, obscuring whether improvements stem from genuine algorithmic innovation, parameter tuning, knowledge recombination, or evaluator overfitting. By instrumenting the search process itself across four evolutionary frameworks, this work enables practitioners to distinguish real capability gains from statistical artifacts, shifting focus from outcome metrics to mechanistic understanding of what these hybrid systems actually learn.

arXiv cs.LG·May 19

62

Illustration for: BalanceRAG: Joint Risk Calibration for Cascaded Retrieval-Augmented Generation

Research Tools & Code

BalanceRAG: Joint Risk Calibration for Cascaded Retrieval-Augmented Generation

BalanceRAG addresses a practical bottleneck in production RAG systems: when to retrieve and when to trust the model alone. Rather than tuning confidence thresholds independently for each pipeline stage, this work jointly optimizes LLM-only and retrieval fallback decisions to hit a target error budget. The insight matters because cascaded RAG is becoming standard in cost-conscious deployments, yet naive threshold-setting leaves performance on the table. Teams building retrieval systems now have a principled framework to trade off latency, retrieval cost, and factuality without conservative stage-by-stage calibration.

arXiv cs.CL·May 19

58

Illustration for: Blackstone to Invest $5B in AI Cloud Company Using Google TPUs

Business & Funding Hardware & Infra

Blackstone to Invest $5B in AI Cloud Company Using Google TPUs

Blackstone's $5 billion commitment to a Google TPU-powered cloud venture signals institutional capital's pivot toward specialized AI infrastructure outside hyperscaler ecosystems. The move reflects growing demand for dedicated compute capacity as enterprises seek alternatives to saturated public cloud offerings, while positioning Google's custom silicon as a competitive moat in the emerging neocloud segment. This partnership underscores how infrastructure constraints are reshaping AI deployment economics and attracting mega-fund capital into previously niche hardware plays.

AI Business·May 19

76

Probability-Conserving Flow Guidance

Researchers have identified a fundamental flaw in how current diffusion and flow models apply guidance: standard methods like Classifier-Free Guidance use ad-hoc linear combinations that violate probability conservation and push samples away from the learned data manifold. By analyzing guidance through the continuity equation, the work decomposes the effect into divergence and score-parallel components, proving the divergence term explodes as sampling nears the data manifold. This motivates a time-dependent correction schedule that preserves geometric integrity. The finding matters because guidance strength is a key lever for balancing sample quality against user control, and this principled approach could improve both without the current tradeoff.

arXiv cs.LG·May 19

62

Illustration for: CopT: Contrastive On-Policy Thinking with Continuous Spaces for General and Agentic Reasoning

Research Models & Releases

CopT: Contrastive On-Policy Thinking with Continuous Spaces for General and Agentic Reasoning

Researchers propose CopT, a reasoning framework that inverts the standard chain-of-thought pipeline by generating draft answers first, then conditioning reflection on those outputs rather than thinking before responding. This addresses a real inefficiency in current LLM reasoning: performative thinking that burns tokens even when models could answer directly. The approach uses continuous embeddings to evaluate draft confidence, potentially reducing inference costs while maintaining reasoning quality. For practitioners, this signals a shift toward more adaptive reasoning strategies that balance speed and accuracy, relevant as inference budgets tighten across production deployments.

arXiv cs.CL·May 19

62

Towards Distillation Guarantees under Algorithmic Alignment for Combinatorial Optimization

Researchers are establishing theoretical guarantees for knowledge distillation when target architectures are deliberately designed to match the underlying algorithmic structure of combinatorial problems. This work extends prior distillation theory by studying graph neural networks aligned with dynamic programming solvers, grounded in the linear representation hypothesis about source model expressivity. The result matters because it bridges learning theory and practical deployment: it clarifies when smaller, inference-efficient models can reliably inherit problem-solving capability from larger teachers, particularly relevant for optimization tasks where architectural alignment is feasible.

arXiv cs.LG·May 19

52

Smooth Partial Lotteries for Stable Randomized Selection

Researchers propose a formal framework for stabilizing randomized selection mechanisms used in high-stakes decisions like funding and hiring. The core insight addresses a real tension in lottery-based allocation: small score fluctuations shouldn't swing selection odds dramatically, yet existing designs fail this test. By imposing Lipschitz smoothness constraints on the probability mapping, the work bridges algorithmic fairness and mechanism design, offering practical guidance for organizations deploying randomization to reduce bias from subjective scoring boundaries. This matters to ML practitioners building selection systems and to institutions adopting algorithmic fairness as policy.

arXiv cs.LG·May 19

58

Illustration for: Tail Annealing for Heavy-Tailed Flow Matching

Tail Annealing for Heavy-Tailed Flow Matching

Researchers propose a coordinate-wise soft-log transformation to enable flow matching models on heavy-tailed data, a long-standing failure mode where standard architectures cannot generate power-law distributions from Gaussian noise. The method compresses extreme values into a tractable range without requiring specialized base distributions or architectural redesign, using a Hill diagnostic to selectively apply the transform only where needed. This addresses a concrete limitation in generative modeling that affects domains like finance, physics, and language modeling where tail behavior matters.

arXiv cs.LG·May 19

58

Illustration for: Text-to-SPARQL Generation with Reinforcement Learning: A GRPO-based Approach on DBLP

Research Models & Releases

Text-to-SPARQL Generation with Reinforcement Learning: A GRPO-based Approach on DBLP

Researchers demonstrate that small language models can learn to translate natural language into SPARQL queries through reinforcement learning, without requiring labeled training data. By applying Group-Relative Policy Optimization to a 1.7B parameter model on scholarly knowledge graphs, the work shows execution-based rewards and structural constraints can substitute for expensive gold annotations. This challenges the prevailing assumption that semantic parsing demands either massive models or full supervision, opening a path for efficient, domain-specific query generation in knowledge-intensive applications.

arXiv cs.CL·May 19

58

Illustration for: Rewarding Beliefs, Not Actions: Consistency-Guided Credit Assignment for Long-Horizon Agents

Rewarding Beliefs, Not Actions: Consistency-Guided Credit Assignment for Long-Horizon Agents

A new reinforcement learning method called ReBel tackles a fundamental bottleneck in training LLM agents for complex, multi-step tasks. The core insight: when agents operate in partially observable environments, their internal beliefs drift and delayed rewards make it hard to pinpoint which decisions actually mattered. ReBel solves this by explicitly tracking belief states and using consistency checks between predicted and observed outcomes as training signals, eliminating the need for external supervision. This addresses a real pain point for anyone building long-horizon reasoning systems, from autonomous planning to interactive dialogue agents.

arXiv cs.CL·May 19

62

Illustration for: PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling

Research Tools & Code

PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling

PromptRad addresses a persistent bottleneck in clinical AI: labeling radiology reports without massive labeled datasets. By reformulating multi-label classification as masked language modeling and injecting medical synonyms from UMLS into the model's verbalizer, the approach sidesteps the need for extensive fine-tuning data. This matters because low-resource constraints are the norm in healthcare, not the exception. The technique signals a broader shift toward prompt-tuning and knowledge injection as practical alternatives to traditional supervised learning in specialized domains where labeled corpora remain scarce.

arXiv cs.CL·May 19

58

Illustration for: Language Mutations Sustain the Persistences of Conspiracy Theories on Social Media

Language Mutations Sustain the Persistences of Conspiracy Theories on Social Media

Computational linguists have identified a critical mechanism by which conspiracy narratives evade detection and suppression on social platforms: semantic mutation. Using three years of X data and survival modeling, researchers found that conspiracy claims undergoing linguistic shifts in pronouns, cognitive framing, and actor-action-target structures persist significantly longer than static variants. The finding has direct implications for content moderation systems and language models trained to detect harmful narratives, suggesting that static pattern-matching approaches may systematically miss evolving conspiracy variants. This work bridges NLP capabilities with platform governance, revealing how language model understanding of semantic drift could improve both detection and intervention strategies.

arXiv cs.CL·May 19

58

Mind Your Moras: Orthography-Aware Error Analysis of Neural Japanese Morphological Generation

Researchers dissect how neural sequence models fail on Japanese morphological inflection, revealing that character-level architectures struggle systematically with hiragana's orthographic encoding of morphophonological rules. By taxonomizing seven distinct error patterns across SIGMORPHON benchmarks, the work exposes a gap between aggregate accuracy metrics and actual linguistic generalization. This matters for practitioners building multilingual NLP systems: high test scores can mask brittle handling of writing systems that encode grammatical information, suggesting that orthography-aware architectures or training regimes may be necessary for robust cross-linguistic morphology.

arXiv cs.CL·May 19

52

Active Context Selection Improves Simple Regret in Contextual Bandits

Researchers have characterized optimal sampling strategies for contextual bandits, proving that active context selection outperforms passive random sampling by a factor related to the context distribution. The work bridges experimental design and online learning, showing that learners can improve regret bounds by allocating exploration proportionally to context frequency raised to the 2/3 power. This advances the theoretical foundations of adaptive decision-making systems that must balance exploration across heterogeneous subpopulations, with implications for recommendation systems and personalized AI that operate across diverse user segments or demographic groups.

arXiv cs.LG·May 19

52

When Critics Disagree: Adaptive Reward Poisoning Attacks in RIS-Aided Wireless Control System

Researchers have identified a novel attack vector against reinforcement learning systems in wireless networks by exploiting disagreement between dual critic networks in Soft Actor-Critic agents. The Disagreement-Guided Reward Poisoning attack targets high-uncertainty decision points where the two critics diverge, corrupting reward signals to push learned policies toward suboptimal behavior. This work exposes a structural vulnerability in a widely-used RL architecture when deployed in safety-critical control domains like spectrum management and RIS-assisted communications, raising questions about the robustness of actor-critic methods in adversarial wireless environments.

arXiv cs.LG·May 19

52

Illustration for: D$^3$-Subsidy: Online and Sequential Driver Subsidy Decision-Making for Large-Scale Ride-Hailing Market

Research Products & Apps

D$^3$-Subsidy: Online and Sequential Driver Subsidy Decision-Making for Large-Scale Ride-Hailing Market

Researchers have developed D3-Subsidy, a diffusion-based controller for real-time driver incentive optimization in ride-hailing networks. The system addresses a core operations-research problem in marketplace AI: balancing dynamic supply and demand under strict budget caps and latency constraints at city scale. Rather than optimizing individual transactions, the approach uses forward-looking sequential decision-making to coordinate subsidy allocation across entire regions. This work bridges reinforcement learning and practical production constraints, offering a template for how ML systems handle multi-objective optimization in high-stakes, real-time consumer marketplaces where traditional per-order methods become computationally prohibitive.

arXiv cs.LG·May 19

58

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