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China Mobile Jiangsu and ZTE unveil intelligent complaint analysis agent to reshape core network O&M
ZTE has joined forces with China Mobile Jiangsu under the guidance of China Mobile's Network Division to pioneer the implementation of core network complaint agent capabilities, marking a significant step forward in accelerating intelligent network operations and maintenance (O&M) transformation. Both parties innovatively introduce the multi-modal signaling model and agent technology to reconstruct the complaint handling process, implement automatic signaling analysis, and efficiently...
If cores are what agents crave, Intel's new Clearwater Xeon 6+ might just quench their thirst
Intel’s Clearwater Forest Xeons were originally designed to power telco networks, SaaS apps, and other high-volume web-scale workloads. But by a stroke of luck, the x86 giant may have also built an agentic AI beast. AI model training, inference, and the GPUs that power them have dominated the discourse for the past few years, but with the rise of agentic harnesses like OpenClaw, CPUs are back in the limelight.
Intel and pals cram 36,864 CPU cores into a 100kW rack while chasing the agentic AI dragon
Intel is working with Foxconn and other infrastructure providers to develop rack-scale reference designs based on the chipmaker’s Xeon processors. Announced during Intel’s Computex keynote on Tuesday, these blueprints aim to provide greater CPU compute densities for running AI agents at scale. While AI models predominantly run on GPUs and other AI accelerators, the agent harnesses, like OpenClaw, which are used to connect them to tools, terminal shells, code interpreters, and other APIs,...
MMSkills: Towards Multimodal Skills for General Visual Agents
Announce Type: replace Abstract: Reusable skills have become a core substrate for improving agent capabilities, yet most existing skill packages encode reusable behavior primarily as textual prompts, executable code, or learned routines. For visual agents, however, procedural knowledge is inherently multimodal: reuse depends not only on what operation to perform, but also on recognizing the relevant state, interpreting visual evidence of progress or failure, and deciding what to do next. We...
From Untrusted Input to Trusted Memory: A Systematic Study of Memory Poisoning Attacks in LLM Agents
arXiv:2606.04329v1 Announce Type: new Abstract: Memory is a core component of AI agents, enabling them to accumulate knowledge across interactions and improve performance. However, persistent memory introduces the risk of memory poisoning, where a single adversarial memory write can exert long-term influence over agent behavior. We present a systematic study of memory poisoning in LLM-based agents.
Rethinking Search as Code Generation
Rethinking Search as Code Generation Evolving search from monolithic services to programmable primitives for the era of agent harnesses. Search is a core primitive for AI systems. Frontier models grow more capable by the month, but they still need access to fresh, accurate, and well-curated knowledge from the wider world.
ADRA-Bank: A Modular Benchmark for Academic Deep Research Agents
arXiv:2512.00986v3 Announce Type: replace Abstract: A surge in academic publications calls for automated deep research (DR) systems, but accurately evaluating them is still an open problem. First, existing benchmarks often focus narrowly on retrieval while neglecting high-level planning and reasoning. Second, existing benchmarks favor general domains over the academic domains that are the core application for DR agents.
EGOSTREAM: A Diagnostic Benchmark for Streaming Episodic Memory in Egocentric Vision
arXiv:2605.31557v2 Announce Type: replace Abstract: Continuous episodic memory is a core capability for autonomous agents operating in dynamic, real-world environments, yet current streaming video benchmarks provide limited tools for diagnosing what models remember and for how long. We introduce Egostream, a diagnostic benchmark for streaming episodic memory evaluation in egocentric vision. \egostream organizes 2,250 curated questions along seven cognitive dimensions: detail, spatial,...
EGOSTREAM: A Diagnostic Benchmark for Streaming Episodic Memory in Egocentric Vision
arXiv:2605.31557v1 Announce Type: new Abstract: Continuous episodic memory is a core capability for autonomous agents operating in dynamic, real-world environments, yet current streaming video benchmarks provide limited tools for diagnosing what models remember and for how long. We introduce \egostream, a diagnostic benchmark for streaming episodic memory evaluation in egocentric vision. \egostream organizes 2,250 curated questions along seven cognitive dimensions: detail, spatial, temporal,...
When LLM Reward Design Fails: Diagnostic-Driven Refinement for Sparse Structured RL
arXiv:2605.28918v1 Announce Type: cross Abstract: For sparse, structured reinforcement-learning tasks with semantic reward-function interfaces, LLM-generated reward shaping is better framed as debugging than one-shot generation. We study PPO-trained agents using MiniGrid as core evaluation and MuJoCo as boundary stress test. Our audit finds two dominant one-shot failure modes -- reward flooding and semantic/API misunderstanding -- plus a rarer weak-shaping case.