arXiv:2606. 26593v1 Announce Type: new Abstract: Email communication has become an integral part of personal and professional life, but handling its vast volume is still a significant issue for large organisations.
arXiv:2610.02627v1 Announce Type: new Abstract: An email assistant should not complete less work simply because a user phrases the same request differently. Yet most benchmarks test each task with on...
arXiv:2608. 07494v1 Announce Type: cross Abstract: AI tools like ChatGPT and DeepSeek, powered by Large Language Models (LLMs), allow users to obtain instant and effective content responses simply by typing requests, such as ``plan a three-day Vienna trip'', ``solve the attached mathematical problem'', ``draft an email to inquire review progress'', etc.
The paper introduces a prompt‑engineering framework that personalizes large language model (LLM) teaching assistants across disciplines by tailoring responses to six learner‑specific dimensions, creating 96 distinct learner profiles. It also analyzes student queries through Bloom’s Taxonomy to gauge cognitive complexity, encoding both learner attributes and cognitive assessments into structured prompts that condition the LLM without retraining. Experiments using NLP metrics and a small human study demonstrate that this approach yields perceptible differences in response style and structure, with statistical evidence linking specific learner attributes to measurable changes.
The article surveys evaluation methods for large language model (LLM)-based agents in multi-turn conversations, reviewing nearly 250 scholarly sources through a PRISMA-inspired framework. It introduces two taxonomies: one outlining what to evaluate—task completion, response quality, user experience, memory and context retention, planning, and tool integration—and another detailing how to evaluate, covering annotation-based, automated, hybrid, and self-judging approaches. The framework incorporates both traditional metrics like BLEU and ROUGE and advanced techniques suited to dynamic, interactive dialogues.
arXiv:2607. 05477v1 Announce Type: cross Abstract: Improving the task performance of Large Language Models (LLMs) is essential, yet scaling these models faces significant challenges such as diminishing returns and high costs.
arXiv:2606. 07909v1 Announce Type: new Abstract: Modern large language model (LLM) agents can use external tools to help users solve complex tasks.
arXiv:2604. 09552v2 Announce Type: replace-cross Abstract: Engineering rulebooks and technical standards contain multimodal information like dense text, tables, and illustrations that are challenging for retrieval augmented generation (RAG) systems.
arXiv:2608.27831v1 Announce Type: cross Abstract: Coding agents are now commonly evaluated on the SWE-bench family of benchmarks, whose tasks are built from curated GitHub issues--long, structured, a...
The study examines how three context‑engineering techniques—semantic layers, orchestration, and compaction—affect data agents that answer questions over heterogeneous data. Using seven large language models on 1,514 long‑horizon tasks, the authors identify three agent behavior groups (investigators, premature answerers, early stoppers) and show that each technique benefits only the group it aligns with, while sometimes harming others. Combining the techniques yields smaller net gains than the sum of individual effects, leading to guidance on selecting techniques based on agent behavior, task demands, and accuracy‑cost trade‑offs.
The paper investigates how different retrieval designs affect conversational agents. Three approaches are compared: skill-based retrieval in the main agent, a non‑persistent retrieval subagent that generates findings, and a persistent retrieval subagent that keeps history and returns evidence to the main agent. The persistent design reduces token usage and API cost by 43.3% and 45.0% respectively, cuts response time by 9.1%, and allows the main agent to request corrections from the subagent.
The paper examines three retrieval designs for conversational agents: skill-based retrieval in the main agent, a non-persistent retrieval subagent that generates findings, and a persistent retrieval subagent that keeps retrieval history and returns evidence directly to the main agent. The persistent design reduces token usage by 43.3% and API cost by 45.0% compared to skill-based retrieval, while cutting average response time by 9.1%. Compared to non-persistent retrieval, it speeds up responses by 15.6 seconds per question but uses 65.1% more tokens and costs 29.1% more.
The paper introduces OPSD, a method that uses on‑policy self‑distillation to train a context‑free student model from a teacher conditioned on user memory, combined with a verify‑then‑regenerate router that directs queries to either weight or text memory. OPSD enables weight memory to compete with or surpass traditional text‑based memory systems on the HaluMem benchmark, reducing token usage per query. The study also explores how different training recipes influence weight memory performance and offers guidance for integrating it with retrieval in persistent, multi‑session agent deployments.