5arXiv cs.CL (Computation and Language)·
ShopX: Foundation model unifying intent understanding and item-space fulfillment for agentic shopping
Researchers from Taobao introduce ShopX, a foundation model that integrates intent understanding, execution planning, and item-space operations (retrieval, ranking, bundling) into a single LLM using semantic IDs (SIDs) rather than wrapping an LLM around existing search pipelines. The system is deployed in agentic shopping workflows via a model-native action protocol with catalog grounding and state management. Evaluated on single- and multi-turn fulfillment tasks derived from Taobao production logs, ShopX outperforms tool-mediated agentic systems, particularly on complex or ambiguous requests. The work is notable as a production-scale deployment case of LLM-native agentic retrieval in e-commerce.
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Taiji: Pareto Optimal Policy Optimization for LLM-enhanced recommendation at Kuaishou scale
Researchers from Kuaishou present Taiji, an LLM-as-Enhancer framework for industrial recommender systems that addresses two bottlenecks: generating high-quality chain-of-thought data via reverse-engineered reasoning and rejection sampling during SFT, and balancing semantic vs. ID-based rewards during RL alignment via a new algorithm called Pareto Optimal Policy Optimization (POPO). The system has been deployed on Kuaishou's advertising platform since May 2026, serving over 400 million daily users. The paper contributes both a practical deployment case study and a novel RL alignment technique for the LLM4Rec paradigm.
LLM-augmented XAI framework with mutual feature interactions for network operations
A new arXiv paper proposes a framework combining LLMs with SHAP-based explainability, augmented by mutual feature interaction data, to generate natural language explanations for AI/ML models used in network operations. The approach is validated on an optical quality-of-transmission estimation task with human evaluators, showing 12.2% and 6.2% improvements in explanation usefulness and scope over a SHAP-only baseline, with 97.5% correctness. The work targets the gap between technical XAI outputs and actionable insights for non-specialist network operators.
Yuxi: Multi-tenant agent harness integrating LightRAG, knowledge graphs, and MCP
Yuxi is an open-source multi-tenant agent harness platform that combines a LightRAG knowledge base with knowledge graph management. Built on LangChain, Vue, and FastAPI, it supports DeepAgents, MinerU PDF parsing, Neo4j, and the Model Context Protocol (MCP). The project has accumulated 5,451 GitHub stars with modest daily traction (+47).
Powering product discovery in ChatGPT
OpenAI is introducing a shopping feature in ChatGPT that enables product discovery and side-by-side comparisons through a new Agentic Commerce Protocol. The update provides visually immersive product browsing and merchant integration directly within the ChatGPT interface. This represents an expansion of ChatGPT's agentic capabilities into e-commerce and transactional workflows.
Position paper proposes micro-transaction markets for verified product information in agentic e-commerce
A preprint from arXiv argues that agent-native micro-payment rails (x402, AP2) shift the bottleneck in e-commerce from product matching to trustworthy information acquisition. The authors envision buyer agents spending fractions of a cent to progressively unlock verified seller and reviewer data under a freemium model with reputational trust scoring. The paper reframes the NLP research agenda for agentic commerce around cost-optimal information acquisition, data pricing, entity resolution, and privacy-preserving persona modelling rather than chat fluency.
PlanBench-XL: Benchmark for LLM Agent Planning in Large-Scale Tool Ecosystems
Researchers introduce PlanBench-XL, an interactive benchmark of 327 retail tasks spanning 1,665 tools designed to evaluate LLM agents on long-horizon planning under retrieval-limited tool visibility. The benchmark includes a blocking mechanism simulating real-world disruptions such as missing or failing tools, forcing agents to detect and recover from broken execution paths. Experiments on ten leading LLMs reveal severe performance degradation: GPT-5.4 drops from 51.90% accuracy in unblocked settings to 11.36% under the most severe blocking condition, highlighting fragility in adaptive planning for large, imperfect tool environments.
AXPO: Agent Explorative Policy Optimization Addresses Thinking-Acting Gap in Multimodal Agentic Reasoning
This paper identifies a structural asymmetry in agentic reasoning called the 'Thinking-Acting Gap,' where tool use is attempted in only ~30% of rollouts under standard RL training (GRPO), and all-wrong tool-using subgroups suppress learning signals. The authors propose AXPO (Agent eXplorative Policy Optimization), which fixes the thinking prefix and resamples tool calls for all-wrong subgroups, combined with uncertainty-based prefix selection. Evaluated across nine multimodal benchmarks on Qwen3-VL-Thinking at multiple scales, SFT+AXPO outperforms SFT+GRPO by +1.8pp on both Pass@1 and Pass@4 at 8B, with the 8B model surpassing the 32B baseline on Pass@4 using 4× fewer parameters.
SPEX and ProxySPEX: Scalable Interaction Discovery for LLM Interpretability
Researchers from BAIR introduce SPEX (Spectral Explainer) and ProxySPEX, algorithms for identifying influential feature, data, and model-component interactions in LLMs at scale. The approach exploits sparsity, low-degreeness, and hierarchy properties to reframe interaction discovery as a sparse recovery problem using tools from signal processing and coding theory. ProxySPEX achieves comparable performance to SPEX with roughly 10x fewer ablations by leveraging hierarchical structure. The methods are evaluated on feature attribution (sentiment analysis), data attribution, and mechanistic interpretability tasks, outperforming marginal methods like LIME at long context lengths.