HiVe-Mem cuts multimodal agent memory tokens by about 90%
A graph of textual and visual anchors plus a trainable evidence selector improved three memory benchmarks across base models.
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A graph of textual and visual anchors plus a trainable evidence selector improved three memory benchmarks across base models.
Belebele recall@1 rises 14.4 to 26.0 points, to 75.2%–77.7%, while English and Russian recall fall.
A Kev-4B gate reduces attack success from 27.9% to 5.7% on a 1,000-question benchmark.

Enterprise Advance customers get it early next year; Nvidia expects faster queries and 90% fewer tokens.
The method leaves architecture, pooling, and contrastive loss unchanged and reports gains on retrieval and long-context benchmarks.
Query-local graph interface induction allows fixed retriever to work on differently built knowledge graphs.
Filler tokens maintain accuracy up to 32K tokens in retrieval but rival statements multiply loss by 9-110 across five open models.
Query, judge, and answer agents are trained with separate rewards, including voting labels and curriculum matching.
Item-space OPD lets 1.7B DecisionRank surpass 27B LLM teacher on TREC DL while running 24.5x faster.
DecisionRank achieves SOTA on TREC DL and BEIR with a 1.7B model that surpasses its 27B generative teacher.
On MiniGrid Memory, a five-token traversal head reached 0.95 mean return after 0.59M steps, ahead of absolute addressing.
A learned world model estimates that quantity from archived rollouts, defining an optimal retrieval set and a TD-target floor.
MemSteer-Opt lifts ATM-Bench Question Score by 13.4 points over Base-BM25 without changing model weights.
A resident 9B backbone adds about 0.1 GiB per skill and beats bge-reranker-v2-m3 on SciFact and NFCorpus after 39 minutes of fine-tuning.
A 120-response pilot found attacker-target adoption in 21 of 24 raw adversarial replies when evidence was supplied.
Authors report average gains of 66.59% on Qwen3-4B and 34.82% on LLaMA3.1-8B across five benchmarks.
The model writes self-generated questions as retrieval keys and later answers user questions from parameters alone, with no external store.
On LoCoMo and MemGallery, authors report macro-F1 gains of 2.23–3.14 points and fewer context tokens than compared memory systems.
Without task-specific training, it beats dense retrieval plus an 8B reranker by 13.8 R@1 on MultiDoc2Dial.
On VMem-Bench, retrieval lifted entity recall from 0.559 to 0.628-0.693 while precision and forbidden-entity avoidance fell.
On 540 messages from 90 cases, retrieval lifted concern-category accuracy from 54.1% to 59.3%.
Stripping PoisonedRAG's question prefix drops reimplemented RAGuard from AUROC 0.953 to 0.758 and raises its miss rate to 0.763.
Ranking training on those heads raises Qwen3-VL-8B page Recall@1 on MMDocIR by 6.03 points.
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