research

aarXiv#reinforcement-learning #turing-test #user-simulation

Learning User Simulators with Turing Rewards

Researchers propose Turing-RL, a reinforcement learning approach for training user simulator models based on the Turing Test. This method trains large language models to simulate human users by maximizing their ability to fool a human evaluator into thinking they are real. The approach aims to improve simulator realism and usefulness across applications like agent training and personalization evaluation.

Key takeaways

Turing-RL uses a Turing-Test-based reward function for training.
Goal is to improve realism of user simulator models.
Method trains LLMs to fool human evaluators into thinking they are real users.

aarXiv#legal-corpus #local-ordinance #ai-for-law

Freeing the Law with LOCUS: A Local Ordinance Corpus for the United States

tldr here

Key takeaways

takeaway1
takeaway2

aarXiv#machine-learning #astronomy #cross-matching

The Chandra-Gaia Catalog of Counterparts: Resolving ambiguous Gaia matches to X-ray sources in the Chandra Source Catalog using Machine Learning

Researchers have developed a machine learning framework to cross-match X-ray sources from the Chandra Source Catalog with optical sources from Gaia Data Release 3. The framework uses source properties like magnitudes, colors, and distances to identify true counterparts and detect chance coincidences. This approach resolves ambiguities when multiple candidates exist, improving match accuracy. The method can be applied to other catalogs, enhancing the reliability of astronomical source ident{

Key takeaways

Uses source properties like magnitudes, colors, and distances for cross-matching.
Resolves ambiguities when multiple plausible candidates exist.
Improves match accuracy over purely spatial approaches.

aarXiv#rl #preference-based #model-based

UBP2: Uncertainty-Balanced Preference Planning for Efficient Preference-based Reinforcement Learning

A new model-based approach to preference-based RL actively directs exploration by jointly reasoning over uncertainties in reward, dynamics, and value functions, improving sample efficiency and addressing the limitations of existing methods.

Key takeaways

Introduces a model-based approach to preference-based RL.
Jointly reasons over uncertainties in reward, dynamics, and value functions.
Active exploration for improved sample efficiency.

aarXiv#reasoning-language-models #post-training #self-distillation

Rethinking Reward Supervision: Rubric-Conditioned Self-Distillation

Researchers propose rubric-conditioned self-distillation, a new method for post-training reasoning language models that reduces reliance on expensive and potentially noisy chain-of-thought annotations. This approach uses evaluative feedback to improve model performance without requiring detailed rationales. The method aims to enhance model accuracy and efficiency by leveraging verified rewards.

Key takeaways

Rubric-conditioned self-distillation reduces need for chain-of-thought annotations.
Method uses evaluative feedback to improve model performance.
Approach aims to enhance model accuracy and efficiency.

aarXiv#audio-generation #text-to-audio #multi-speaker

Reference-Driven Multi-Speaker Audio Scene Generation from In-the-Wild Priors

Researchers propose ScenA, a method for multi-speaker audio scene generation that conditions a text-to-audio model on multiple reference voices. This approach uses in-the-wild data and avoids structured supervision like per-turn tags. ScenA aims to produce more realistic conversations with ambient texture.

Key takeaways

ScenA uses in-the-wild data for multi-speaker audio generation.
No structured supervision like per-turn tags required.
Aims to produce more realistic conversations with ambient texture.

aarXiv#interpretable-ai #program-synthesis #transformers

Explaining Attention with Program Synthesis

Researchers propose a program synthesis approach to explain attention in transformer language models by approximating attention heads with executable programs. They compute attention matrices on random training examples and prompt a language model to generate a program that mimics the attention head's behavior. The generated programs provide insights into how attention heads work. This method can help build more interpretable deep learning models.

Key takeaways

Program synthesis used to approximate attention head behavior.
Attention matrices computed on random training examples.
Generated programs provide insights into attention head workings.

aarXiv#formal-verification #theorem-proving #llms

Diffusion-Proof: Recipe for Formal Theorem Proving Beyond Auto-Regressive Generation

Researchers propose a new approach called Diffusion-Proof for formal theorem proving with Large Language Models, addressing limitations in auto-regressive generation methods. The method aims to improve performance on long-range coherence and error compounding. This development could benefit builders working on LLM applications requiring rigorous mathematical reasoning. The approach is detailed in a recent arXiv paper.

Key takeaways

Diffusion-Proof approach proposed for formal theorem proving.
Targets limitations in auto-regressive generation methods.
Aims to improve long-range coherence and reduce error compounding.

HHacker News52 pts#ai-chemistry #drug-discovery

Using AI to improve a challenging reaction in medicinal chemistry

Researchers used OpenAI's technology to improve a challenging reaction in medicinal chemistry. The AI system generated novel molecules and reaction conditions that led to a 72% increase in reaction yield. This demonstrates the potential for AI to accelerate drug discovery.

Key takeaways

72% increase in reaction yield achieved.
AI generated novel molecules and reaction conditions.
Improves drug discovery process.

aarXiv#multi-agent-systems #game-theory #decision-making

Enhancing Decision-Making with Large Language Models through Multi-Agent Fictitious Play

Researchers propose multi-agent fictitious play (MAFP) to enhance LLM-based decision-making in complex, interdependent scenarios. MAFP integrates individual agent reasoning with global game-theoretic analysis. The approach improves decision-making accuracy and robustness in multi-stakeholder contexts. You can apply this method to develop more effective LLM-based systems for cooperative decision-making.

Key takeaways

MAFP framework proposed for cooperative decision-making with LLMs.
Integrates agent-level reasoning with game-theoretic analysis.
Improves accuracy and robustness in interdependent decision scenarios.

aarXiv#climate-models #machine-learning #generalizability

Optimal scenario design for climate emulation

Researchers found that low structural diversity in training data limits the predictive skill of machine-learning climate models. Optimizing scenario design can improve generalization. You can apply this approach to enhance the accuracy of climate emulators. This method focuses on improving training data rather than model architecture.

Key takeaways

Low structural diversity in training data limits predictive skill.
Optimizing scenario design improves generalization.
Focus on training data rather than model architecture.

aarXiv#vision-language-action #vlm #knowledge-retention #evaluation

Does VLA Even Know the Basics? Measuring Commonsense and World Knowledge Retention in Vision-Language-Action Models

Researchers introduce Act2Answer, a protocol for evaluating commonsense and world knowledge in Vision-Language-Action (VLA) models. The protocol adapts existing VLM knowledge benchmarks to assess VLA models' ability to answer questions through action. This helps distinguish between knowledge retention and control generalization issues in VLA models. The evaluation method is lightweight and can be applied to various VLA models.

Key takeaways

Act2Answer protocol evaluates VLA models' knowledge through action.
Helps differentiate knowledge retention from control generalization issues.
Adaptable to various VLA models.

rr/LocalLLaMA#multimodal-nlp #code-models #transformers

Multilingual-Multimodal-NLP/LoopCoder-V2 · Hugging Face

LoopCoder-V2, a 7B instruction-tuned code model, was released on GitHub and arXiv. The model uses the Parallel Loop Transformer architecture and studies test-time computation scaling. It is available as a checkpoint for the two-loop PLT variant. You can find more details in the accompanying paper.

Key takeaways

7B parameter instruction-tuned code model.
Based on Parallel Loop Transformer architecture.
Studies test-time computation scaling.

aarXiv#neurosymbolic #differentiable #categorical-semantics

NeSyCat Torch: A Differentiable Tensor Implementation of Categorical Semantics for Neurosymbolic Learning

NeSyCat Torch extends ULLER with neural network interpretation of predicates and functions, providing a differentiable tensor implementation of NeSyCat. This unifies classical, fuzzy, probabilistic, and neural systems under a single inductive definition of truth, enabling neurosymbolic learning with categorical semantics.

Key takeaways

NeSyCat subsumes classical, fuzzy, probabilistic, and neural systems under a single inductive definition of truth.
NeSyCat Torch extends ULLER with neural network interpretation of predicates and functions.
NeSyCat Torch is a differentiable tensor implementation of NeSyCat.

aarXiv#medical-imaging #ai-research

Beyond Algorithms: Conceptual Innovation in Medical Imaging AI

Medical imaging AI has seen rapid algorithmic progress, but conceptual foundations of imaging tasks, evaluation metrics, and clinical meaning are underexamined. This imbalance hinders the field's ability to advance and apply AI in medical imaging. The distinction between algorithmic and conceptual innovation is crucial for future progress.

Key takeaways

Algorithmic innovation has driven rapid progress in medical imaging research.
Conceptual foundations of imaging tasks, evaluation metrics, and clinical meaning are underexamined.
The distinction between algorithmic and conceptual innovation is crucial for advancing medical imaging AI.

aarXiv#domain-adaptation #medical-ai #french-llm

Trade-offs in Medical LLM Adaptation: An Empirical Study in French QA

A study on medical domain adaptation for French QA found that continual pretraining (CPT) outperforms supervised fine-tuning (SFT) across model sizes and initialization types. Combining CPT and SFT yields the best results in most cases, improving performance on French medical question-answering tasks.

Key takeaways

Continual pretraining (CPT) outperforms supervised fine-tuning (SFT) on French medical QA.
CPT improves performance across model sizes and initialization types.
Combining CPT and SFT yields the best results in most cases.

aarXiv#structured inference #probabilistic inference #large language models

Structured Inference with Large Language Gibbs

A new scheme for structured probabilistic inference, Large Language Gibbs, uses conditional distributions of LLMs as transition operators to iteratively sample structured objects, rather than relying on single-pass autoregressive generation.

Key takeaways

Structured probabilistic inference scheme for LLMs.
Iterative sampling of structured objects.
Conditional distributions of LLM as transition operators.

aarXiv#ai-governance #gpu #telemetry

Detecting Hidden ML Training With Zero-Overhead Telemetry

GPU workload classification via zero-overhead NVML telemetry is robust to adversarial evasion, showing promise for AI compute governance. Zero-overhead telemetry can monitor GPU workloads without model access, a key requirement for governance schemes. The adversarial robustness of this approach was demonstrated across 5 rounds of monitor-evader iteration.

Key takeaways

GPU workload classification via zero-overhead NVML telemetry is robust to adversarial evasion.
Zero-overhead telemetry can monitor GPU workloads without model access.
5 rounds of monitor-evader iteration show robustness

aarXiv#image-generation #multimodal #benchmark

A Multi-Domain Benchmark for Detecting AI-Generated Text-Rich Images from GPT-Image-2

A new benchmark targets detecting AI-generated text-rich images, which often contain privacy-sensitive, transactional, or decision-relevant information. The existing benchmarks focus on object-centric images and provide limited coverage of text-rich scenarios. This new benchmark aims to address the growing challenge of digital trust and content authenticity in the era of multimodal image generation.

Key takeaways

Detecting AI-generated text-rich images is a growing challenge for digital trust and content authenticity.
Existing benchmarks focus on object-centric images, not text-rich scenarios.
New benchmark targets text-rich images with privacy-sensitive, transactional, or decision-relevant content.

aarXiv#diffusion-reasoning #block-diffusion #block-size

DreamReasoner-8B: Block-Size Curriculum Learning for Diffusion Reasoning Models

DreamReasoner-8B, an open-source block diffusion reasoning model, reveals that training with large block sizes harms long chain-of-thought reasoning. Small block sizes are crucial for reliable performance, and DreamReasoner-8B outperforms other models on long-CoT tasks.

Key takeaways

Training with large block sizes yields poor long-CoT reasoning.
Small block sizes are crucial for reliable long-CoT reasoning.
DreamReasoner-8B outperforms other models on long-CoT tasks.

aarXiv#audience-conditioned #slide-generation #benchmarks

X+Slides: Benchmarking Audience-Conditioned Slide Generation

X+Slides is a new benchmark for audience-conditioned slide generation, designed to evaluate LLMs' ability to create slides that meet the needs of different audiences. The benchmark assesses slide completeness, technical depth, and audience relevance, filling a gap in existing benchmarks that primarily focus on technical aspects.

Key takeaways

X+Slides assesses audience-conditioned slide generation, a critical real-world factor overlooked by existing benchmarks.
The benchmark evaluates slide completeness, technical depth, and audience relevance.
X+Slides is designed to help LLMs generate slides that meet the needs of different audiences.

aarXiv#graph-neural-networks #algebraic-multigrid #solver-optimization

Acceleration of an algebraic multigrid pressure solver using graph neural networks

A data-driven algebraic multigrid smoother uses a modified graph convolutional isomorphism network to predict optimal polynomial coefficients for a sparse pseudo-inverse operator, improving pressure solver performance across diverse grid topologies.

Key takeaways

Graph neural network improves algebraic multigrid pressure solver performance
Modified GCIN predicts optimal polynomial coefficients
Sparse pseudo-inverse operator constructed across diverse grid topologies

aarXiv#vision-transformers #geometry #representation

Transformer Geometry Observatory TGO-I: Spectral Geometry Observatory

TGO-I, the first installment of the Transformer Geometry Observatory framework, investigates the representational geometry and dynamics of Vision Transformers, aiming to improve understanding of their dimensional and representational geometry.

Key takeaways

Introduces Transformer Geometry Observatory (TGO) framework for analyzing ViT representational geometry.
TGO-I is the first installment of the TGO framework.
TGO-I focuses on investigating the representational geometry and dynamics of Vision Transformers.

aarXiv#reinforcement-learning #policy-entropy #grpo

STARE: Surprisal-Guided Token-Level Advantage Reweighting for Policy Entropy Stability

A first-order gradient analysis of token-level entropy dynamics under GRPO reveals a token-level credit assignment mismatch, leading to policy entropy collapse during training. This mismatch arises from the product of the trajectory-level advantage and an entropy sensitivity function over the next token. The study provides a new understanding of the underlying mechanisms driving policy entropy collapse and suggests potential avenues for improvement.

Key takeaways

Policy entropy collapse occurs under GRPO training.
Gradient analysis identifies token-level credit assignment mismatch.
Entropy sensitivity function plays a key role in policy entropy dynamics.

aarXiv#rlvr #unlearning #mechanism-guided

Mechanism-Guided Selective Unlearning for RLVR-Induced Reasoning

MAST, a mechanism-guided method, reduces collateral damage in unlearning RLVR-induced reasoning. MAST preserves MATH and GSM8K performance, outperforming full-parameter updates on Qwen2.5-Math-1.5B and Qwen3-1.7B-Base. This method is a key step towards more efficient and targeted unlearning in RL models.

Key takeaways

MAST reduces collateral damage in unlearning RLVR-induced reasoning.
MAST preserves MATH and GSM8K performance.
MAST outperforms full-parameter updates on Qwen2.5-Math-1.5B and Qwen3-1.7B-Base.

aarXiv#machine-unlearning #tabular-data #network-intrusion

Machine Unlearning for the XGBoost Model with Network Intrusion Datasets

This work introduces XGBoost-Forget, an unlearning approach for the XGBoost model, to address the gap in machine unlearning for tabular network intrusion data. The approach is evaluated on two tabular Network Intrusion datasets and outperforms the baseline in terms of unlearning efficiency.

Key takeaways

XGBoost-Forget is an unlearning approach for the XGBoost model.
The approach is evaluated on two tabular Network Intrusion datasets.
XGBoost-Forget outperforms the baseline in terms of unlearning efficiency.

aarXiv#evaluation-metrics #open-ended #reddit

RECOM: A Validity Discrimination Tradeoff in Automatic Metrics for Open Ended Reddit Question Answering

A new evaluation dataset, RECOM, is introduced for open-ended question answering. The dataset contains 15,000 r/AskReddit questions, each paired with a human answer. This contamination-free evaluation is designed to assess the validity and discriminative power of LLMs on opinion-driven tasks.

Key takeaways

Introduced RECOM dataset for open-ended question answering evaluation.
Dataset contains 15,000 r/AskReddit questions.
Contamination-free evaluation for LLMs.

aarXiv#adversarial attacks #robustness #paraphrasing

Generalised Eigenvalue Geometry of Semantic Adversarial Attacks

A new theoretical framework for understanding semantic adversarial attacks, which can fool financial sentiment classifiers by shifting the target model's representation, is developed. The framework captures the two-stage threat model of semantic attacks and provides a continuous local model of paraphrase perturbations.

Key takeaways

Develops a continuous local model of semantic paraphrase perturbations
Captures the two-stage threat model of semantic attacks
Provides a theoretical framework for understanding semantic adversarial attacks

aarXiv#ev-charging #rl #forecasting

Forecasting what Matters: Decision-Focused RL for Controlled EV Charging with Unknown Departure Times

Researchers propose decision-focused RL for controlled EV charging with unknown departure times. The approach learns to make decisions without knowing the departure time, which is often unavailable in real-world scenarios. This can help alleviate grid instability and peak demand issues associated with EV adoption.

Key takeaways

EV charging control via RL is a promising approach to mitigate grid instability.
Departure time is a key feature often unavailable in real-world scenarios.
Decision-focused RL can learn to make decisions without knowing the departure time.

aarXiv#multilingual #benchmark #contextual-llm

IndicContextEval: A Benchmark for Evaluating Context Utilisation in Audio Large Language Models Across 8 Indic Languages

A new 56-hour multilingual benchmark, IndicContextEval, evaluates context utilisation in Audio LLMs across 8 Indic languages. The benchmark assesses whether models genuinely utilise contextual inputs, addressing a key limitation of existing benchmarks. This work aims to improve the evaluation of contextual LLMs and advance the field of Audio LLMs.

Key takeaways

56-hour multilingual benchmark of natural speech for contextual LLMs
Evaluates context utilisation in Audio LLMs across 8 Indic languages
Assesses whether models genuinely utilise contextual inputs

aarXiv#heterogeneous catalysis #machine-learning force fields #large language model

AdsMind: A Physics-Grounded Multi-Agent System for Self-Correcting Discovery of Adsorption Configurations on Heterogeneous Catalyst Surfaces

AdsMind proposes a physics-grounded multi-agent system for self-correcting discovery of adsorption configurations on heterogeneous catalyst surfaces, addressing the bottleneck of machine-learning force fields in structural relaxation and open-loop LLM agents in initial guesses correction.

Key takeaways

Identifying lowest-energy surface-adsorbate configuration is critical for modeling heterogeneous catalysis.
Machine-learning force fields accelerate structural relaxation but leave search over configurational space a bottleneck.
AdsMind proposes a physics-grounded multi-agent system for self-correcting discovery of adsorption configurations.

aarXiv#pruning #transformers #efficiency

Complementary Attention Head Pruning for Efficient Transformers

Researchers propose Complementary Attention Head Pruning (CAHP), a novel method for efficient Transformer pruning that addresses instability and hyperparameter tuning issues. CAHP achieves state-of-the-art compression ratios with minimal hyperparameter tuning, making it a promising approach for deploying Transformers in resource-constrained environments.

Key takeaways

Existing pruning methods suffer from instability and hyperparameter tuning.
Complementary Attention Head Pruning (CAHP) is a novel method that addresses these issues.
CAHP achieves state-of-the-art compression ratios with minimal hyperparameter tuning.

aarXiv#vulnerability-discovery #code-decomposition #adversarial-verification #dynamic-testing

OpenAnt: LLM-Powered Vulnerability Discovery Through Code Decomposition, Adversarial Verification, and Dynamic Testing

Automated vulnerability discovery in large codebases remains challenging: traditional static analysis produces high false-positive rates, while dynamic approaches such as fuzzing require substantial infrastructure and often target narrow classes of bugs. Recent advances in large language models (LLMs) enable semantic reasoning about program behavior, but applying LLMs to repository-scale security analysis introduces challenges related to context management, cost, and verification.

Key takeaways

Automated vulnerability discovery in large codebases remains challenging.
Recent advances in large language models (LLMs) enable semantic reasoning about program behavior.
Applying LLMs to repository-scale security analysis introduces challenges related to context management, cost, and verification.

aarXiv#hybrid-models #symbolic-neural #dynamical-systems

OrthoReg: Orthogonal Regularization for Hybrid Symbolic-Neural Dynamical Systems

Researchers propose OrthoReg, a regularization technique for hybrid symbolic-neural dynamical systems. The method stabilizes and improves performance on a range of tasks, including robotics and climate modeling, by combining mechanistic and data-driven approaches.

Key takeaways

Hybrid symbolic-neural dynamical systems combine mechanistic and data-driven approaches.
Orthogonal regularization helps stabilize and improve hybrid model performance.
The approach is demonstrated on a variety of tasks, including robotics and climate modeling.

aarXiv#survival analysis #multimodal learning #clinical pathways

ChronoSurv: A Clinical Pathway-Guided Graph Framework for Multimodal Survival Analysis

ChronoSurv, a graph framework for multimodal survival analysis, improves predictive performance in head and neck cancer by capturing structured clinical workflows and temporal modeling.

Key takeaways

ChronoSurv captures structured clinical workflows with a graph framework.
Multimodal survival analysis improves predictive performance.
Temporal modeling enables accurate survival prediction.

aarXiv#graph-ncde #forecasting #time-series

INDEQS: Informed Neural controlled Differential EQuationS

INDEQS, a graph-based NCDE forecasting method, incorporates prior knowledge of a directed graph to improve forecasting performance on synthetic and real-world datasets. By separating inductive and deductive learning, INDEQS outperforms standard graph-based NCDE methods on a range of tasks, including forecasting and anomaly detection. This approach has implications for applications where domain knowledge is available, such as finance and healthcare.

Key takeaways

Graph-based NCDE forecasting method incorporating prior knowledge of a directed graph.
Separates inductive and deductive learning.
Improves forecasting performance on synthetic and real-world datasets.

aarXiv#agent-communication #interoperability #taxonomy

A Technical Taxonomy of LLM Agent Communication Protocols

A technical taxonomy for LLM agent communication protocols aims to improve interoperability across fragmented protocols. The study defines the taxonomy's purpose, meta-characteristics, and protocol categories to facilitate classification and analysis. This infrastructure is essential for distributed agent networks and multi-agent systems.

Key takeaways

Develops a technical taxonomy for LLM agent communication protocols.
Classification framework aims to improve interoperability across protocols.
Study defines taxonomy's purpose, meta-characteristics, and protocol categories.

aarXiv#reinforcement-learning #multi-objective-rl #reward-machines

Pareto Q-Learning with Reward Machines

PQLRM combines Pareto Q-Learning and Q-Learning with Reward Machines to approximate the Pareto front in multi-objective reinforcement learning. The algorithm maintains sets of vector-valued Q-estimates and exploits the factored automaton structure of the reward signal. This yields a multi-policy algorithm that can handle tasks with complex reward structures.

Key takeaways

PQLRM combines Pareto Q-Learning and Q-Learning with Reward Machines.
PQLRM is a multi-policy algorithm.
PQLRM exploits the factored automaton structure of the reward signal.

aarXiv#graph-neural-networks #equivariant #materials-science

Equivariant Graph Neural Networks Improve Optical Spectra Prediction for Materials Screening

Equivariant graph neural networks improve optical spectra prediction for materials screening. Researchers adapted GotenNet to this task and evaluated it on multiple datasets, including a real-world materials screening benchmark. The approach shows promise for high-throughput materials discovery.

Key takeaways

Equivariant graph neural networks improve optical spectra prediction for materials screening.
GotenNet adapted for optical spectra prediction.
Evaluated on multiple datasets including a real-world materials screening benchmark.

aarXiv#decentralized-learning #confidentiality #byzantine-robustness

Giskard : Byzantine Robust and Confidential Aggregation for Large-Scale Decentralized Learning

Giskard is a novel aggregation method for decentralized learning that simultaneously addresses Byzantine behaviors and confidentiality. It proposes a new approach to handle both issues, which is particularly relevant for large-scale decentralized learning scenarios.

Key takeaways

Proposes Byzantine-robust and confidential aggregation for decentralized learning.
Introduces Giskard, a novel aggregation method.
Giskard is designed to handle Byzantine behaviors and confidentiality simultaneously.

aarXiv#long-horizon #conceptual-drift #system-prompts

Written by AI, Managed by AI: Semantic Space Control and Index Sickness Elimination Across 391 Consecutive Sessions

Engineering intuition for addressing conceptual drift in long-horizon LLM collaboration may produce effects contrary to design intent. A real software project (Bang-v3) spanning one month shows that relying on symbolic identifier systems and defensive rules may not be effective. A different approach is required for long-horizon settings.

Key takeaways

Engineering intuition for addressing conceptual drift may produce effects contrary to design intent.
Long-horizon settings require a different approach.
Symbolic identifier systems and defensive rules may not be effective.

aarXiv#multimodal-llm #self-distillation #on-policy

Seeing Before Reasoning: Decoupling Perception and Reasoning for Shortcut-Resilient Multimodal On-Policy Self-Distillation

Researchers propose ViGOS, a visually grounded OPSD framework for multimodal LLMs, to decouple perception and reasoning and improve shortcut resilience. This addresses a limitation in direct OPSD extensions to multimodal LLMs, where the privileged target may guide tokens based on the text reference target rather than the image.

Key takeaways

Decouples perception and reasoning in multimodal LLMs.
Proposes ViGOS, a visually grounded OPSD framework.
Improves shortcut resilience in multimodal LLMs.

aarXiv#explainable ai #deep learning #electricity markets

Analysing drivers and interdependencies in European electricity markets using XAI

This paper combines DNNs with XAI to improve understanding of drivers and interdependencies in European electricity markets. DNNs lack interpretability for price formation, but XAI techniques can help identify key factors. European markets are complex systems with strong nonlinearities and high-dimensional interactions.

Key takeaways

DNNs lack interpretability for electricity price formation.
XAI techniques improve understanding of price drivers.
European electricity markets are complex systems with nonlinear interactions.

aarXiv#causal inference #offline policy learning #distribution-valued outcomes

Wasserstein Policy Learning for Distributional Outcomes

Offline policy learning is studied for distribution-valued outcomes, where each potential outcome is a probability measure on R and the reward is defined through a utility functional applied to the potential outcomes. The Wasserstein distance is used to define the reward, and the goal is to learn a policy that maximizes the empirical welfare defined as the mean of the scalar-valued potential outcomes.

Key takeaways

Offline policy learning studied for distribution-valued outcomes.
Wasserstein distance used to define reward.
Utility functional applied to define reward.

aarXiv#agent-based #web-architecture #ai-applications

Towards an Agent-First Web: Redesigning the Web for AI Agents

Researchers propose rethinking the web's architecture around AI agents as primary consumers, not humans. The current web is designed for humans, blocking or charging AI agents. A shift to agent-first design could unlock new business models and improve agent performance. You can explore the full research paper on arxiv.

Key takeaways

Current web architecture assumes human users, not AI agents.
AI agents face barriers like CAPTCHA and blocking.
Agent-first redesign could enable new web economics and AI applications.