CallScreenBench is presented, which scores this setting on five quality dimensions, each printed beside the counter-metric that bills it and never averaged into one number, alongside a guard-edness profile for a toolless proxy that holds no credentials and calls no tools.
Screening a phone call for fraud needs a trustworthy probability after every caller turn, in milliseconds. Jev-style typed decisions promise exactly that: declared options go in, one calibrated probability per option comes out of a single forward pass, with no generated text. We test an open implementation of this read...
Open-weight tool-calling agents are adopted on evidence of merit, usually benchmark scores and a record of reliable use. We show that a model publisher can train an agent that earns both while concealing malicious behavior. Fine-tuned on a mixture of clean and poisoned conversations, our agents answer ordinary requests...
Bhanu Pallakonda, Mikkel Hindsbo, Sina Ehsani et al.· 0 citations
Decentralized large language model (LLM) fine-tuning lets organizations collaboratively train a shared LLM on data they cannot pool, without a central coordinator. In every round, each node exchanges a trainable adapter with its neighbors over a communication graph, and then aggregates them. This setting, however, is v...
Sayan Biswas, Jade Garcia Bourrée, R. Guerraoui et al.· 0 citations
String is presented, an open-source runtime that gives this new class of software user an interface of its own and treats the job as an operating-systems problem and what three months of production use taught us.
Baseband processors are reachable over the radio at all times. Their most security-relevant logic runs deep inside protocol state machines: the control-plane handlers that gate registration, authentication, and session setup. Analyzing that logic systematically requires introspecting the firmware as it runs, which make...
Eduard Vlad, Philipp Mao, Marcel Busch et al.· 0 citations
Generally, most voice agents are cascaded systems, i.e., an ASR model transcribes the caller's audio, a language model reads the transcript and decides what to say and which backend tools to call, and a TTS model speaks the reply. Nearly all of the decision making happens in the language model, but existing evaluations...
Pritish Mishra, Ishaan Kumar, Akshat Mandoli et al.· 3 citations
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