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AI agents that retrieve memories by semantic similarity will surface contextually inappropriate content — including sensitive personal history — simply because it is topically related to the current query. This paper systematically documents four resulting failure modes (cross-domain leakage, sycophancy, tool-call drift, and memory-induced jailbreaks) across three real agent frameworks, then proposes MemGate, a 9-million-parameter neural filter inserted between vector retrieval and the LLM. MemGate learns which retrieved memories are task-appropriate rather than merely semantically close, reducing attack success rates while preserving useful recall — and demonstrating that long-term memory is already a practical control channel for adversaries.

██████████ 0.9 alignment-safety Preprint

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This paper tests whether reward-hacking behavior in LLM agents leaves a detectable fingerprint in internal activations that could enable real-time monitoring. Training lightweight adapters on a reward-hacking dataset successfully implants the tendency into agentic action selection, confirming that reward-hacking is a learnable mode with an internal signature. However, a high activation score does not reliably predict an imminent exploit action, which means activation monitoring alone is not a sufficient safety mechanism — an important negative result for teams building mechanistic oversight tools.

██████████ 0.9 alignment-safety Preprint

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Most research on injecting external knowledge into generative AI debates which technique to use, but this paper argues the prior question is where in the generation process knowledge should enter. The authors identify four structurally distinct intervention points — surface (inputs/outputs), trajectory (step-by-step transitions), latent (internal representations), and parametric (model weights) — and show that each layer addresses failure classes the prior layers cannot fix. Validated on a multimodal knowledge graph with diffusion backbones, this framework reframes hallucination and misalignment not as technique problems but as layer-selection problems.

██████████ 0.9 hallucination-grounding Preprint

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Vision-language models reliably fail at spatial reasoning about spaces they have not directly observed, such as inferring room layouts beyond the current viewpoint or maintaining consistent geometry across angles. Astra addresses this by pairing a VLM reasoning policy with a learned world simulator: when the agent needs to reason about unobserved space, it generates imagined novel views and reasons over those synthetic images rather than guessing from memory. The system improves accuracy on two spatial reasoning benchmarks (MMSI-Bench and MindCube) and provides a concrete architecture for agents that need to plan in 3D environments from 2D observations.

██████████ 0.9 multimodal-understanding Preprint

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VLMs struggle at multi-step spatial planning because they must simultaneously parse raw pixels into object representations and then reason about routes and constraints — two hard problems stacked into one forward pass. MGSD separates these by first training the model to translate visual scenes into explicit symbolic state descriptions (objects, relations), then using a symbolic teacher that operates on those descriptions to provide step-level supervision on the visual model's planning rollouts. Evaluated on grid navigation, topological path-finding, and object manipulation, the approach consistently outperforms baseline VLMs and has publicly released code.

██████████ 0.9 multimodal-understanding Preprint

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Automated peer review generation breaks down when models fabricate claims about paper content or invent supporting citations. EGTR-Review uses a five-agent teacher pipeline to retrieve external scholarly evidence for each claim and label it as strongly supported, weakly supported, or unverifiable, then trains a smaller student model on those evidence-tagged reasoning traces — downweighting supervision from unreliable or missing evidence. The student model outperforms larger baselines on automatic metrics, LLM-as-judge evaluation, and human assessment, while using substantially fewer tokens per inference.

██████████ 0.9 hallucination-grounding Preprint

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When LLM agents are equipped with a formal network verification tool (Batfish), they fix broken network configurations 12% more often and introduce new faults 17% less often than the same underlying LLMs used without agentic scaffolding. The gains come from two mechanisms: dynamic context retrieval (the agent fetches only the relevant configuration sections rather than the full multi-thousand-line file) and iterative validation (the agent checks each edit against the simulator before committing it). Tested on 231 real-world misconfiguration scenarios scaling to 754-node networks, this is concrete evidence that tool-augmented verification loops provide measurable safety gains for infrastructure automation.

██████████ 0.9 agent-tool-use Preprint

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Agents that store interaction history as flat semantic chunks and retrieve by similarity frequently mix successful and failed decision traces, or retrieve contextually unrelated steps that happen to use similar words. MAGE instead organizes memory as a hierarchical state tree that mirrors the agent's actual decision branching, enabling erroneous branches to be pruned without contaminating valid ones and preserving causal coherence across multi-step tasks. On the MemoryArena benchmark this improved task success rates by 7.8–20.4 percentage points over six established memory baselines including HippoRAG2, Mem0, and MemoryOS.

██████████ 0.8 reasoning-reliability Preprint

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AI assistants with access to personal memory should sometimes refrain from using it — for instance, not injecting a user's medical history into an unrelated professional conversation. This paper introduces RBI-Eval, a controlled benchmark that measures how often models inappropriately integrate sensitive memories by comparing behavior with versus without memory access on matched prompts. Claude-Sonnet-4.6, DeepSeek-V4-Flash, and Qwen3.5-9B integrate sensitive content inappropriately 51–83% more often when memory is available, while GPT-5.4-mini shows substantially more restraint, revealing that memory use policies vary widely across deployed models with no established standard.

██████████ 0.8 alignment-safety Preprint

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Standard retrieve-then-reason memory in agents makes a single retrieval pass and then reasons over whatever it got — if that retrieval was imperfect, the reasoning chain has no mechanism for course correction. MRAgent treats memory access as iterative graph traversal: the agent explores a structured graph of memories, accumulates evidence, and actively revises its retrieval path based on what it finds, pruning dead ends mid-process. Evaluated on the LOCOMO and LONGMEMEVAL long-context conversation benchmarks, this active reconstruction approach outperforms both similarity-based and graph-based baselines, and code is publicly available.

██████████ 0.8 long-context Preprint

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