Reading an AI's "chain of thought" to predict its behavior? Turns out that's not very reliable 🔮 The fresh idea: make behavior prediction itself a learning task. Title: Forecasting Future Behavior as a Learning Task URL: 🔮 Overview A method to predict how large reasoning models (LRMs) will behave on new inputs. Instead of relying on explicit explanations, it introduces trainable "Behavior Forecasters" that analyze a single reasoning trajectory to predict outputs. ❓ Challenges Solved We want to understand and predict LRM behavior, but prior approaches have limits. ・Existing explanation methods don't scale to long reasoning trajectories ・Read as natural language, those trajectories are often unreliable A model's written "thoughts" don't necessarily reflect its actual behavior. 💡 Methodology & Proposed Approach ・It treats behavior prediction itself as a learnable task ・Training data comes directly from querying LRMs — no human annotation needed ・It runs in a single forward pass at inference ・Instantiated on two tasks: estimating answer consistency across reruns, and predicting how input modifications affect outputs ・End-to-end fine-tuning of the backbone, and initializing from the target LRM's weights, proved essential 📊 Experimental Results ・Behavior Forecasters outperform GPT-5.4 and Claude Opus-4.6 as "naive readers" ・And they achieve higher accuracy at a small fraction of the inference cost #LLMInterpretability# #ReasoningModels#

For agent memory, the real question isn't "how to store" — it's "what to remember" 🧠 A fresh take that learns what to memorize via reinforcement learning. Title: Task-Focused Memorization for Multimodal Agents URL: 🧠 Overview This work proposes TaskMem, which treats long-term memory for multimodal agents as a learnable policy optimized with reinforcement learning, focused on deciding what to memorize. From an unbounded stream of observations, it selectively retains only the content relevant to the agent's role and task. ❓ Challenges Solved A multimodal agent operating in the real world continuously receives an unbounded stream of observations. ・Most prior work focused on how to store memories (designing memory modules) ・But the essential problem is what to memorize — without a principled way to select role-relevant content from an endless stream, memory simply fails This work starts from that shift in perspective. 💡 Methodology & Proposed Approach TaskMem treats memorization as a learnable policy, optimized in two phases. ・Phase 1: learn high-quality memorization under fidelity requirements ・Phase 2: post-deployment fine-tuning that uses task rewards to align memorization with the environment's demands ・It builds on the MLLM Qwen3-VL-30B-A3B and optimizes the policy lightly via adapter tuning ・Reward models derived from real tasks steer the policy toward selecting relevant content 🌍 Use Cases / Experimental Results On reformulated streaming benchmarks, it delivered clear accuracy gains. ・VideoMME: 67.9% VQA accuracy (+6.3%) ・EgoLife: 45.4% VQA accuracy (+7.0%) ・EgoTempo: 27.6% VQA accuracy (+5.3%) ・Strong precision across all benchmarks (80.5-85.6%) It charts a practical path for long-running, always-on agents to selectively remember the right things while keeping context bloat in check. #AIAgents# #Memory#