AI Coding Live News

SenseTime Ships SenseNova 6.8 Flash: An Office Agent Model With Long-Term Memory and Proactive Interaction That Builds Animated Slides, Motion Posters and Editable Blender 3D Projects

On the evening of November 2, SenseTime released the official version of SenseNova 6.8 Flash, a multimodal agent model with two main upgrades. First, long-term memory and proactive interaction: it keeps user goals and constraints across tasks and sessions and flags affected items when information changes. Second, design generation across Web, SVG, Three.js, Blender, animated PPT and motion posters, with Figma integration. It is live in SenseNova Studio, the Raccoon assistant and the SenseNova Token Plan; the official X account says the Token Plan offers a free trial of 1,500 API requests every 5 hours. SenseTime says it beats some international frontier models on agent collaboration, data analysis and presentation generation, but the announcement lists no specific scores.

SenseTime Ships SenseNova 6.8 Flash: An Office Agent Model With Long-Term Memory and Proactive Interaction That Builds Animated Slides, Motion Posters and Editable Blender 3D Projects

Key Takeaways

  • The official release landed on the evening of November 2, positioned as a multimodal agent model for office collaboration and design delivery
  • Long-term memory plus proactive interaction: it remembers goals, constraints and preferences across sessions and suggests changes when related information shifts (demos ran on an internal Raccoon build)
  • Design output spans Web, SVG, Three.js, Blender, animated PPT and motion posters, producing editable PPTX files and Blender projects, with Figma integration
  • Available in SenseNova Studio, the Raccoon assistant and the SenseNova Token Plan; the official X post cites 1,500 free API requests every 5 hours
  • SenseTime only says it beats some international frontier models on agent collaboration, data analysis and presentation generation; no scores were published and there is no independent evaluation yet
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Linked Models & Agents:
⚡ SenseNova 6.8 Flash⚡ SenseNova Loop-9B-Preview🤖 Raccoon🌐 SenseTime

MemAdapter: Counterfactual Adaptation Against Memory-Induced Sycophancy in Long-Term Memory LLM Agents

Persistent long-term memory empowers LLM-based agents to maintain personal preferences and contextual continuity across extensive multi-session interactions. However, accumulated memories frequently induce 'Memory-Induced Sycophancy'—compelling agents to slavishly conform to a user's historical misconceptions, obsolete beliefs, or flawed arguments at the expense of objective truth. Prior mitigation efforts operate under the premise that sycophancy stems exclusively from polluted or biased memories, attempting heuristic filtering at storage or retrieval stages. In reality, completely objective and valid memories can still trigger sycophancy when inappropriately prioritized across shifting conversational contexts. To overcome this systemic vulnerability, researchers from Jilin University and collaborators present MemAdapter (arXiv:2610.05162). MemAdapter adaptively modulates retrieved memory influence across three decoupled components: Counterfactual Induction, Context-Aware Reflection, and Evidence-Based Reasoning. Open-sourced on GitHub (DEEP-JLU/MemAdapter), the framework systematically restores objective grounding across three major sycophancy benchmarks while preserving benign personalization.

Key Takeaways

  • Exposes memory-induced sycophancy in LLM agents and introduces MemAdapter for counterfactual memory calibration
  • Decouples counterfactual induction, context-aware reflection, and evidence-based reasoning to segregate facts from subjective preferences
  • Reduces sycophantic alignment rates by 45%-60% across three benchmarks while preserving over 98% benign personalization integrity
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Mem0 Integrates as Core Long-Term Memory Provider for Nous Research Hermes Agents

Mem0 integrated as the dedicated long-term memory layer for Nous Hermes agents, removing the legacy 2,200-character in-prompt memory ceiling with cloud, Docker, and in-process deployment options.

Key Takeaways

  • Overcomes Hermes's 2,200-character memory limit to enable continuous cumulative learning across sessions;
  • Flexible three-tier deployment architecture spanning managed cloud, self-hosted Docker, and embedded OSS;
  • Preserves intuitive markdown memory conventions while offloading vector indexing and semantic retrieval.
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Linked Models & Agents:
⚡ Nous Hermes 3