Papers27

#retrieval-augmented generation

A Mechanistic View on Video Generation as World Models: State and Dynamics

Luozhou Wang, Zhifei Chen et al.Jan 22arXiv

This paper says modern video generators are starting to act like tiny "world simulators," not just pretty video painters.

#world models#video generation#state representation

Toward Efficient Agents: Memory, Tool learning, and Planning

Intermediate

Xiaofang Yang, Lijun Li et al.Jan 20arXiv

This survey explains how to make AI agents not just smart, but also efficient with their time, memory, and tool use.

#agent efficiency#memory compression#tool learning

M^4olGen: Multi-Agent, Multi-Stage Molecular Generation under Precise Multi-Property Constraints

Intermediate

Yizhan Li, Florence Cloutier et al.Jan 15arXiv

The paper introduces M^4olGen, a two-stage system that designs new molecules to match exact numbers for several properties (like QED, LogP, MW, HOMO, LUMO) at the same time.

#molecular generation#multi-property optimization#fragment-level editing

Collaborative Multi-Agent Test-Time Reinforcement Learning for Reasoning

Intermediate

Zhiyuan Hu, Yunhai Hu et al.Jan 14arXiv

This paper introduces MATTRL, a way for multiple AI agents to learn from their own conversations at test time using short, reusable text notes instead of retraining their weights.

#multi-agent systems#test-time reinforcement learning#experience retrieval

The AI Hippocampus: How Far are We From Human Memory?

Intermediate

Zixia Jia, Jiaqi Li et al.Jan 14arXiv

This survey asks how close AI memory systems are to human memory and organizes the answer into three parts: implicit memory (inside the model), explicit memory (outside storage you can look up), and agentic memory (what an AI agent keeps over time to plan and act).

#LLM memory#implicit memory#explicit memory

Lost in the Noise: How Reasoning Models Fail with Contextual Distractors

Intermediate

Seongyun Lee, Yongrae Jo et al.Jan 12arXiv

The paper shows that when we give AI lots of extra text, even harmless extra text, it can get badly confused—sometimes losing up to 80% of its accuracy.

#NoisyBench#Rationale-Aware Reward#RARE

KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions

Intermediate

Tingyu Wu, Zhisheng Chen et al.Jan 8arXiv

KnowMe-Bench is a new test that checks if AI helpers truly understand a person, not just remember facts.

#person understanding#autobiographical narratives#cognitive stream

COMPASS: A Framework for Evaluating Organization-Specific Policy Alignment in LLMs

Intermediate

Dasol Choi, DongGeon Lee et al.Jan 5arXiv

COMPASS is a new framework that turns a company’s rules into thousands of smart test questions to check if chatbots follow those rules.

#policy alignment#allowlist denylist#enterprise AI safety

Scaling Open-Ended Reasoning to Predict the Future

Intermediate

Nikhil Chandak, Shashwat Goel et al.Dec 31arXiv

The paper teaches small language models to predict open-ended future events by turning daily news into thousands of safe, graded practice questions.

#open-ended forecasting#calibrated prediction#Brier score

AI Meets Brain: Memory Systems from Cognitive Neuroscience to Autonomous Agents

Intermediate

Jiafeng Liang, Hao Li et al.Dec 29arXiv

This survey links how human brains remember things to how AI agents should remember things so they can act smarter over time.

#agent memory#episodic memory#semantic memory

FaithLens: Detecting and Explaining Faithfulness Hallucination

Intermediate

Shuzheng Si, Qingyi Wang et al.Dec 23arXiv

Large language models can say things that sound right but aren’t supported by the given document; this is called a faithfulness hallucination.

#faithfulness hallucination#hallucination detection#explainable AI

Does It Tie Out? Towards Autonomous Legal Agents in Venture Capital

Intermediate

Pierre Colombo, Malik Boudiaf et al.Dec 21arXiv

Capitalization tie-out checks if a company’s ownership table truly matches what its legal documents say.

#capitalization tie-out#dataroom#cap table verification

1 2 3