arXiv AI
Jul 21
Just A Rather Very Intelligent Spoken Agent
arXiv:2607. 16610v1 Announce Type: new Abstract: Long-horizon AI agents are becoming increasingly capable, yet their interaction with users remains surprisingly thin.
PUBG Ally: A Conversational Embodied Agent as an AI Teammate
PUBG Ally is an embodied, voice‑enabled AI teammate for PUBG: BATTLEGROUNDS that can perceive the game world, interpret player speech, and autonomously decide actions while keeping speech synchronized with gameplay. It combines a language‑model agent that uses a controlled interface to gather game information and a faster control layer for movement, combat, and recovery. The system was trained on nearly 39,000 real‑player sessions and evaluated through player feedback and preference comparisons, with live deployment requiring low‑latency on‑device execution and safety safeguards.
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arXiv AI
Sep 3
CivBench: A Long-Horizon Benchmark for Tool-Mediated Agents in Civilization VI
CivBench is an open‑source benchmark that evaluates language‑model agents in the long‑horizon, tool‑mediated game Civilization VI using the Model Context Protocol (MCP). Each episode lasts over 300 turns, generating thousands of tool calls across a 76‑tool action space, and includes a narration layer that translates visual game state into structured text. The study characterises agent behaviour across four model families, introducing Proactive Monitoring Rate (PMR) and RAG@10 as interface‑level metrics, and finds that agents often under‑monitor strategic state and fail to execute near‑term commitments despite tool access and explicit guidance.
arXiv Machine Learning
Jun 5
CollabBench: Benchmarking and Unleashing Collaborative Ability of LLMs with Diverse Players via Proactive Engagement
arXiv:2606. 05793v1 Announce Type: cross Abstract: While LLM-based agents excel at individual tasks, effective collaboration with realistic human partners remains challenging.
arXiv AI
Jul 24
HARP: The Human--AI Research Platform
arXiv:2607. 20773v1 Announce Type: cross Abstract: Large language models (LLMs) have shifted human--computer interaction from `traditional'' interface journeys toward more conversational exchanges.
arXiv Computation and Language
Sep 23
Qwen-Audio-3.1-Realtime: Towards Reliable Agentic Voice Interaction
arXiv:2609.25176v1 Announce Type: cross Abstract: Real-time voice assistants must reason over evolving requests, execute actions, and follow conversational rules. Qwen-Audio-3.1-Realtime brings these...
arXiv AI
Aug 25
GameXpert-Bench: How Far Are Coding Agents from Expert Game Development?
arXiv:2608.21833v1 Announce Type: new Abstract: Recent large language models (LLMs) can operate as coding agents that build complete games from natural language requests. Game development is especial...
arXiv AI
6d ago
Evaluating Real-Time Voice Agents: From Component Quality to Grounded Outcomes
The paper reviews the fragmented literature on real‑time voice agents, noting that architecture, turn‑taking, and agentic evaluation communities rarely cite each other. It presents three evidence‑based claims: (1) architecture choice is a deployment constraint rather than a definitive solution, (2) evaluation has shifted from component quality to grounded outcomes, and (3) the dyadic assumption is breaking down as multiparty turn‑taking and reasoning become essential. The authors propose the TRG reporting standard to characterize agents by timing, recovery, and state‑verified outcomes, with an optional fourth axis for multiparty contexts.
arXiv AI
3d ago
Talk2Agent: Benchmarking Voice Interfaces for Text Agents
arXiv:2609.38867v1 Announce Type: new Abstract: Large language model (LLM) computer-use agents are typically evaluated with clean written instructions, despite speech being an increasingly popular in...
arXiv AI
Jun 30
GPTNT: Benchmarking Real-Time Collaboration Between Multimodal Agents on Keep Talking And Nobody Explodes
arXiv:2606. 28514v1 Announce Type: new Abstract: Multimodal models are increasingly deployed to solve tasks collaboratively with humans or other artificial agents.
arXiv AI
Sep 2
Exploring Collaboration between a language and a non-language agent
arXiv:2609.00474v1 Announce Type: cross Abstract: LLMs are increasingly deployed as orchestrators that coordinate specialized subagents to solve complex tasks through natural language. However, in ma...
arXiv AI
Jul 13
Embodied Multi-Agent Coordination by Aligning World Models Through Dialogue
arXiv:2605. 12920v3 Announce Type: replace-cross Abstract: Effective collaboration between embodied agents requires more than acting in a shared environment; it demands communication grounded in each agent's evolving understanding of the world.
arXiv Machine Learning
Aug 31
Acquire, Repair, Preserve: A Diagnosis-Guided Post-Training Recipe for Small-Model Dialogue Game Agents
The paper presents a post‑training recipe for small dialogue‑game agents that involves three steps: acquiring broad game participation via supervised fine‑tuning, repairing specific local failures with turn‑local preference pairs, and preserving general capabilities. Applied to the LM Playschool Challenge, the method raises the public clemscore from 10.67 to 38.92 and the closed in‑domain score from 13.41 to 41.17 while keeping overall static performance nearly unchanged. The gains are mainly within the targeted game family, with limited improvement on out‑of‑domain clemscore.