{"_canonicalization":{"envelope_id":"axm_ + sha256(envelope minus {signature, axiom_id, anchors})","envelope_signature":"ed25519(envelope minus {signature, axiom_id})","json":"sort_keys=True, separators=(',',':'), ensure_ascii=False, allow_nan=False, utf-8","leaf_hash":"sha256(0x00 || canonical_json(envelope_full))","seal_signature":"ed25519(seal minus {signature, sig_algorithm})"},"axiom_id":"axm_a96b3edc138db69b45f3ca8d630380e769a0fffe24d209583c6f1b7d5105160b","bitcoin_anchor":{"bitcoin_attestations":[],"calendar_attestations":[],"ots_url":"","stamped_at":"","status":"pending_next_stamp"},"envelope":{"anchors":[{"chain":"crovia.axiom_graph","height":0,"merkle_proof":"spider_vendor_press_v1","root_at_anchor":"spider_vendor_press_v1"}],"axiom_id":"axm_a96b3edc138db69b45f3ca8d630380e769a0fffe24d209583c6f1b7d5105160b","axiom_type":"AX.OBS","body":{"axiom_subtype":"news.vendor_press.v1","category":"news","fingerprint":"9064db16561282c93e3259c7c948b5bbd65390a3b7c0aee786aa67ec8023cfd4","published":"Fri, 29 May 2026 00:00:00 -0400","receipt_hash":"9064db16561282c93e3259c7c948b5bbd65390a3b7c0aee786aa67ec8023cfd4","schema":"spider.news.vendor_press.v1","spider":"vendor_press","spider_record":{"axiom_subtype":"news.vendor_press.v1","category":"news","decision_hint":"POSITIVE","envelope_target":"AX.OBS","fingerprint":"9064db16561282c93e3259c7c948b5bbd65390a3b7c0aee786aa67ec8023cfd4","observed_at":"2026-05-29T04:43:58.478092Z","parent_run_hash":"0fcd87efcfe67ccb9952f747541debc16793919a4d20fd71ca0ad5516a0a13ee","published":"Fri, 29 May 2026 00:00:00 -0400","runtime_version":"0.1.0","schema":"spider.news.vendor_press.v1","source_status":200,"source_url":"https://export.arxiv.org/rss/cs.AI","spider":"vendor_press","summary_excerpt":"arXiv:2605.29027v1 Announce Type: new \nAbstract: The use of Large Language Models (LLMs) is proliferating, yet their performance is observed to vary based on prompting styles and tones. In this study, we investigate both whether and how tonal variations in prompts lead to disparate LLM accuracy for objective multiple-choice questions. We use two datasets: a 50-base question dataset with five tone variants and a 570-base question MMLU subset spanning 57 subjects with seven tone variants. Experiments were conducted to evaluate the performance of four cost-efficient, popular LLMs: ChatGPT-4o, ChatGPT-5-nano, Gemini 2.5 Flash, and Gemini 2.5 Flash Lite. Across models, tonal effects are systematic but highly model-dependent. Some models show small, yet statistically significant, shifts, while others exhibit large accuracy swings across tones. Further, we identify subject-level differences in tone sensitivity and present a routing framework to explain how tones may attune internal reasoning ","title":"Mind Your Tone: Does Tone Alter LLM Performance?","url":"https://arxiv.org/abs/2605.29027","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.29027v1 Announce Type: new \nAbstract: The use of Large Language Models (LLMs) is proliferating, yet their performance is observed to vary based on prompting styles and tones. In this study, we investigate both whether and how tonal variations in prompts lead to disparate LLM accuracy for objective multiple-choice questions. We use two datasets: a 50-base question dataset with five tone variants and a 570-base question MMLU subset spanning 57 subjects with seven tone variants. Experiments were conducted to evaluate the performance of four cost-efficient, popular LLMs: ChatGPT-4o, ChatGPT-5-nano, Gemini 2.5 Flash, and Gemini 2.5 Flash Lite. Across models, tonal effects are systematic but highly model-dependent. Some models show small, yet statistically significant, shifts, while others exhibit large accuracy swings across tones. Further, we identify subject-level differences in tone sensitivity and present a routing framework to explain how tones may attune internal reasoning ","title":"Mind Your Tone: Does Tone Alter LLM Performance?","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-05-29T04:43:58Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2605.29027"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:ce3638b0bc5d38bd22fbad06a465c01f9079b0a0e6661ecc132eb02678f159fd2bccf5d7ec4058a1ccd06fbcb0847c29b2b67e4842f92c2d5831eca4f66e3e0b","signer":"crovia.substrate","subject":{"observed_at":"2026-05-29T04:43:58Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2605.29027"},"tsa":{"authority":"crovia.substrate.bootstrap","rfc3161_token":"{\"kind\":\"crovia.bootstrap.tsa\",\"source_jsonl\":\"/opt/crovia/spider/data/news/vendor_press_v1.jsonl\",\"source_seal_merkle_root\":\"spider_vendor_press_v1\",\"upgrade_path\":\"Sessione H \\u2014 OpenTimestamps weekly anchor\"}"},"zk_mode":"clear","zk_proof":null},"ledger":{"leaf_hash":"a757035fb097760c1952606c4fc8170b7c1c6be60dcd7ab4dc57b195381f3e45","leaf_index":157655,"ledger_path":"/opt/crovia/substrate/axiom_ledger.jsonl"},"merkle_proof":{"hash_alg":"sha256","leaf_prefix":"0x00","node_prefix":"0x01","odd_leaf_rule":"duplicate_last","path":[{"sibling":"0dd5a6a47c6b5968ddd2bb104802d2fcc190a016ee6626bb2e1b72d27719dc04","side":"left"},{"sibling":"68aea7294c1151b8db42f3bc5bbd3aa91853f95edb7a15c3e48ff8b256ccbf56","side":"left"},{"sibling":"dc47d7bc60756368bb6ff20a3399c83adb9f51059a8120eb0d73727a57c50c21","side":"left"},{"sibling":"5d5db581b90d223c2cc94102e6ab6383e4d97f9eb4c3e6899f42a6d610c64a57","side":"right"},{"sibling":"bb9691a4bac0bf4ae76e52675fbd3b05bc40d48fca7c797c7f066a157c7f880a","side":"left"},{"sibling":"043b0b93db31688bb506b27c0bb88b9838dd4a0b5e0340f6fa82e708ed75922b","side":"right"},{"sibling":"f65c4257e1e3e5a8d1432a336a21d8e806501313e0c2888ff215dba80f4c4cfe","side":"left"},{"sibling":"27e5f392a568b11659ecd5aed52502b5e6d88eea7862dffac806a2d7b5a9ef78","side":"left"},{"sibling":"ece7b043912367e6be374d77e09128de032af83a2b87e39b397350dd279ebe83","side":"left"},{"sibling":"39ec45a73732c5ed1abe96972bd3bc32a517083704467dde1c8117578609124d","side":"left"},{"sibling":"68b4895a8015cdde1c1baeaf63a8382cf574ce4d2ad88d596d4217ce2238245e","side":"left"},{"sibling":"0bc831354843fa27f7ba6a4b3080a72fd440b9a76d1bac63429adfbfe6549bca","side":"right"},{"sibling":"995b421824624a8282c7f44e64c64ee35344800f477ae1845b41be14d3fab94c","side":"right"},{"sibling":"66331bac84ca0f8983eb09fac7eaf95af234f1b82680b793eabff4ee25caac40","side":"left"},{"sibling":"35ca36cee447f0ef7064a25d55f59357c66901e31427729c4c1d8b14aa8adb6c","side":"left"},{"sibling":"eef0e8906a749d3470f89beeedc723f37a5737010bbb0dcc7cf91515338e5a3e","side":"right"},{"sibling":"1a07e481a9407d71aad078ce854cdeee362163c887fe10f889b0ecf0b5e749ad","side":"right"},{"sibling":"d841ad93efda0869e5eb97678f348f03f5caab4353e05ff4bf18f47fb945b822","side":"left"}]},"schema":"crovia.axiom_proof.v1","seal":{"first_collector_run_id":"","first_receipt_hash":"","jsonl_path":"/opt/crovia/substrate/axiom_ledger.jsonl","key_id":"430895f101d38164","last_collector_run_id":"","last_receipt_hash":"","leaf_count":158251,"merkle_root":"485e6b31fe60c8beba5b394808c7e4c32448b2ff65c2482c480ca0e2a2eda718","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260529T053701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-05-29T05:37:37Z","sig_algorithm":"ed25519","signature":"bbf9f005201182fce4f9d94c7a9d01a508b56daf7d9611bd73514f5f616bc059d0e5e1f2edfc95716e6fe08ef5fae38b558cbf7f2fd8f9d5dfe4c34a54c83005","signer_version":"1.1.0"},"trust_root":{"key_id":"430895f101d38164","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","signature_algorithm":"ed25519","url":"/registry/canon/TRUST_ROOT.md"},"verifier":{"spec":"/registry/canon/AXIOM_RECEIPT_v1.md","url":"/v/axm_a96b3edc138db69b45f3ca8d630380e769a0fffe24d209583c6f1b7d5105160b"}}