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Multimodal models increasingly reach for tools when solving visual tasks (crop, zoom, rotate, brighten), a paradigm known as thinking-with-images. The central challenge is one of perception: tools mostly serve to expose visual evidence, reasoning over that evidence stays in language, and most targets are ones a human c...
A typed decision is a choice among a fixed set of options, returned as a probability rather than as text. Systems that need typed decisions today use models trained for that purpose. This report describes AnyJev, which reads a typed decision from one prefill of a pretrained instruction-tuned language model. The readout...
Long-horizon agents require context compression to manage growing interaction histories. Compression quality, however, is ultimately determined by downstream execution. Existing prompt-adaptation methods infer compression errors by comparing full-context and compressed trajectories. Such comparisons cannot isolate indi...
SPECTRA is developed, a training-free, drop-in codec that re-encodes the cache into a coordinate system and concentrates the bit budget on the channels that carry the signal, pushing usable compression past the 2-bit cliff so the same GPU holds longer contexts and larger batches.
TRACE is introduced, a verifier-guided framework that evaluates individual compaction events through paired closed-loop continuations from the same environment state and uses summary preferences to optimize a natural-language compression prompt while keeping all models frozen.
The results show that skill representation itself matters, and that simply preserving the structure already present in skill files can substantially improve retrieval.
MV-WSA (Marginal-Value Working-Set Allocation), which splits memory by marginal latency benefit per byte while enforcing a KV-admission floor, is implemented in WiSP (Working-Set Paging), a routing-aware expert pager that plugs into an unmodified serving engine and preserves byte-identical outputs.
A unified semantic modeling framework powered by a small language model (SLM) to address the challenges of job understanding in structured and unstructured contexts and provides practical insights into building industry-scale text understanding systems.
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