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This paper benchmarks the serving cost of three agentic memory systems (Mem0, Hindsight, Mastra Observational Memory) against reference strategies across conversational backbones, finding that cost is driven by internal memory behavior, break-even points vary widely, and no system wins on both cost and accuracy.
This paper introduces GenAI Evaluation, a governed and configuration-driven pipeline for scalable multi-dimensional evaluation of retail conversational agents. It achieves high accuracy using LLM-as-a-judge scoring with selective re-evaluation, validated against human-labeled data.
ProACT introduces a breakdown-aware agent framework for multi-user collaboration, where the agent proactively detects collaboration breakdowns and decides whether to intervene. The paper also presents the first multi-user collaboration benchmark for evaluating such proactive agents.
This paper introduces a taxonomy of conversational memory types and a user-centric evaluation framework to study how different memory roles affect response quality in RAG-based conversational agents.
A developer documents the architecture of an AI agent runtime built for a SaaS company, focusing on safety, tool execution, state management, and separation of reasoning from execution.
ElevenLabs introduces the ability to call your Hermes Agent, enabling voice-based interaction with AI agents through their platform.
SaliMory is a framework that trains a single language model to manage cognitively-structured memory (user facts, preferences, and working memory) for conversational agents, using hierarchical stage-wise process rewards and reward-decomposed contrastive refinement. It reduces memory-attributed failures by one-third, outperforms state-of-the-art by over 10% in end-to-end accuracy, and more than doubles the Good Personalization rate.
Proposes Structure-Aware RAG (SA-RAG), which uses tables as an intermediate structured representation to reduce noise in retrieval-augmented generation for conversational agents, with quality-aware metadata generation and two table generation methods, outperforming existing baselines on noisy real-world datasets.
This paper presents a multimodal emotion recognition module for proactive conversational agents, using facial recognition and linguistic analysis. A user study with 20 participants reveals a 'poker face' effect where visual cues are unreliable, while linguistic analysis proves more accurate; the study also shows agents can elicit emotions through conversational adaptation.
When2Speak is a synthetic dataset and pipeline for training LLMs to decide when to speak in multi-party conversations. Fine-tuning on this dataset significantly improves turn-taking, with reinforcement learning reducing missed interventions from 50% to ~20%.
Huggingface introduces EcomRLVE-GYM, a framework providing eight verifiable environments for training reinforcement learning agents on complex e-commerce tasks. The tool features adaptive difficulty curricula and algorithmic rewards to improve task completion in shopping assistants, demonstrated by training a Qwen 3 8B model.