arXiv AI
Knowing the User Is Not Knowing What We Established: Grounding Awareness in Personal Agents
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arXiv Computation and Language
Sep 21
MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks
MemoryArena is a new evaluation gym that benchmarks agent memory in interdependent multi‑session tasks. Unlike prior benchmarks that test memorization or single‑session action in isolation, MemoryArena requires agents to acquire memory while interacting with the environment and then use that memory to guide future decisions across a range of tasks such as web navigation, planning, information search, and formal reasoning. The benchmark reveals that agents excelling on existing long‑context memory tests perform poorly here, highlighting a gap in current memory evaluation methods.
arXiv AI
Sep 3
AdaMem: Learning What to Remember with Adaptive Memory Policies for Personalized Agents
AdaMem introduces adaptive memory policies that allow personalized agents to decide what information to write into long‑term memory based on user preferences for each interaction context. Each policy is updated from periodic feedback and controls subsequent memory writing, aiming to improve relevance and reduce unnecessary memory persistence. In experiments on AdaMem‑Bench, AdaMem raises QA accuracy from 80.0% to 84.35% while cutting persistent memory by 9.27%, though models still struggle to execute policies reliably.
arXiv AI
Jun 16
ACCORD: Action-Conditioned Contextual Grounding for Language Agents
arXiv:2606. 16432v1 Announce Type: cross Abstract: User instructions are often underspecified because humans rely on implicit assumptions about the surrounding environment.
arXiv Computation and Language
Oct 27
TellOnce: Correcting Coding Agents Once, Improving Their Future Actions
arXiv:2606.13174v2 Announce Type: replace-cross Abstract: Interactive LLM agents increasingly work with users across tasks, yet remembering a previous correction does not ensure that the agent change...
arXiv AI
Jul 15
PM-Bench: Evaluating Prospective Memory in LLM Agents
arXiv:2607. 12385v1 Announce Type: new Abstract: A significant challenge in agentic AI is prospective memory: the ability to execute an intention at a specific future cue or state while other activities are ongoing.
arXiv AI
Oct 19
Testing the Planning Claims of LLM-Based Agents against Formal Semantics
The paper evaluates the planning abilities of large language model (LLM) agents by comparing their performance against formal semantics defined in hidden PDDL models. Across five domains, four LLMs, six prompting strategies, and three agent architectures, the best setup solved 78% of solvable tasks offline but only 40% online, with limited recognition of unsolvable tasks and no clear benefit from more elaborate prompting. The study concludes that claims of LLM planning competence require validated action traces rather than end‑to‑end success rates.