AI Agents

[P] Implemented ACT-R cognitive decay and hyperdimensional computing for AI agent memory (open source)

Built a memory server for AI agents (MCP protocol) and implemented two cognitive science techniques in v7.5 I wanted to share. ACT-R Cogn...

"They operate like slot machines": AI agents are scrambling power users' brains

Considering NeurIPS submission [D]

Wondering if it worth submitting paper I’m working on to NeurIPS. I have formal mathematical proof for convergence of a novel agentic sys...

[2509.25424] Polychromic Objectives for Reinforcement Learning

The paper introduces polychromic objectives for reinforcement learning, enhancing policy diversity and exploration in pretrained models, ...

[2602.03003] Open Problems in Differentiable Social Choice: Learning Mechanisms, Decisions, and Alignment

The paper discusses differentiable social choice, a framework integrating machine learning with social choice theory, identifying 18 open...

[2601.21468] MemOCR: Layout-Aware Visual Memory for Efficient Long-Horizon Reasoning

MemOCR introduces a multimodal memory agent that enhances long-horizon reasoning by using layout-aware visual memory, optimizing context ...

[2509.22387] SpinGPT: A Large-Language-Model Approach to Playing Poker Correctly

SpinGPT introduces a novel approach using large language models to enhance poker strategies, particularly in the Spin & Go format, achiev...

[2601.18467] OffSeeker: Online Reinforcement Learning Is Not All You Need for Deep Research Agents

The paper introduces OffSeeker, a model demonstrating that offline training can effectively replace costly online reinforcement learning ...

[2601.04568] Neurosymbolic Retrievers for Retrieval-augmented Generation

The paper presents Neurosymbolic Retrievers for Retrieval-augmented Generation, addressing the limitations of traditional RAG systems by ...

[2509.20648] Wonder Wins Ways: Curiosity-Driven Exploration through Multi-Agent Contextual Calibration

This paper presents CERMIC, a novel framework for enhancing multi-agent exploration in reinforcement learning by calibrating intrinsic cu...

[2512.20798] A Benchmark for Evaluating Outcome-Driven Constraint Violations in Autonomous AI Agents

This paper introduces a benchmark for evaluating outcome-driven constraint violations in autonomous AI agents, highlighting safety concer...

[2512.17898] Humanlike AI Design Increases Anthropomorphism but Yields Divergent Outcomes on Engagement and Trust Globally

This study explores how humanlike AI design influences user engagement and trust across different cultures, revealing that anthropomorphi...

[2512.06393] Conflict-Aware Fusion: Resolving Logic Inertia in Large Language Models via Structured Cognitive Priors

This article introduces Conflict-Aware Fusion, a framework designed to address Logic Inertia in large language models (LLMs) by integrati...

[2509.04575] Bootstrapping Task Spaces for Self-Improvement

This article presents Exploratory Iteration (ExIt), a novel approach in reinforcement learning that enhances self-improvement in agents b...

[2512.01149] A Benchmark of Causal vs. Correlation AI for Predictive Maintenance

This paper benchmarks causal versus correlation-based AI methods for predictive maintenance, revealing that while correlation models exce...

[2510.27623] BEAT: Visual Backdoor Attacks on VLM-based Embodied Agents via Contrastive Trigger Learning

The paper presents BEAT, a novel framework for executing visual backdoor attacks on Vision-Language Model (VLM)-based embodied agents, hi...

[2510.25232] From Medical Records to Diagnostic Dialogues: A Clinical-Grounded Approach and Dataset for Psychiatric Comorbidity

This article presents a novel approach to psychiatric comorbidity through the creation of a large-scale dataset and a multi-agent diagnos...

[2508.14734] AFABench: A Generic Framework for Benchmarking Active Feature Acquisition

AFABench introduces a benchmark framework for Active Feature Acquisition (AFA), addressing the need for standardized evaluation of AFA me...

[2510.16658] Foundation and Large-Scale AI Models in Neuroscience: A Comprehensive Review

This comprehensive review explores the impact of large-scale AI models on neuroscience, detailing their applications in neuroimaging, bra...

[2510.15862] Rethinking the Design of Reinforcement Learning-Based Deep Research Agents

This paper explores the design of reinforcement learning-based deep research agents, emphasizing key design choices that enhance performa...

[2510.12066] AI Agents as Universal Task Solvers

The paper discusses AI agents as stochastic dynamical systems, emphasizing their ability to learn and reason through transductive inferen...

[2510.04373] JEF-Hinter: Leveraging Offline Knowledge for Improving Web Agents Adaptation

The paper presents JEF-Hinter, a system designed to enhance the adaptation of web agents by leveraging offline knowledge, improving perfo...

[2509.26209] Diversity-Incentivized Exploration for Versatile Reasoning

The paper presents DIVER, a framework for enhancing reasoning in Large Language Models through diversity-incentivized exploration, addres...