This article highlights a common problem in local LLMs where they incorrectly classify real-time information beyond their knowledge cutoff as fictional or satirical, even when provided with tools, often due to excessive RLHF training.
Many local models have a problem (that raised due to excessive RHLF training): They mostly think that everything that is beyond their knowledge cutoff date would be "fictional" or "satirical". To be fair: Even the Gemini API without web access can have this sometimes. But it stops when you give it tools. However, with many local models, sadly it even goes on WITH tools: Look at this: >You search the web for iran war 2026 news gemma-4-26B-A4B-it-Q4\_K\_M\_128k >Based on the search results, there is a significant amount of content describing a "2026 Iran War." However, it is important to note that these search results appear to be from a fictional or speculative scenario, likely part of a "future history" exercise, a geopolitical simulation, or a creative writing project. of today, there is no real-world war occurring or scheduled for 2026 involving Iran. >Key Observations from the Search Results: As The search results contain specific dates and details that suggest they are part of a narrative or simulation: >Specific Dates: Several snippets mention specific dates in 226 (e.g., "February 28," "April 17, 2026," and "May 1, 2026") describing events like "U.S.-Israeli joint strikes." >Operation Names: One result mentions "Operation Epic Fury," a name used within the context of this specific 2026 scenario. >Nature of the Content: While the snippets appear in formats resembling real news (e.g., Wikipedia, CSIS, Atlantic Council), they describe events that have not happened in actual history. This type of content is common in geopolitical forecasting simulations or "alternate history"-style academic exercises used to study potential risks and consequences. >Summary of the "Scenario" described in the results: >If you are looking for information on this specific geopolitical simulation, the "news" within that narrative includes: >The Conflict: A joint U.S.-Israeli military operation against Iran. >Core Drivers: Fears of Iran becoming a nuclear power and the disruption of the existing regional security order. >Impact: The scenario explores impacts on global energy security (oil markets), the shift toward a fragmented global order, and changes in Middle Eastern power dynamics. >Conclusion: There is no actual news of an Iran war in 2026. You are likely seeing results from a geopolitical simulation or speculative "future history" analysis. So the tool call worked, "Epic Fury" is there, still the model thinks it's fictional or "future". Sometimes it can be fixed with including "It is x.x.2026" in the system prompt, but it is a workaround that shouldn't be necessary.
An article summarizing Anthropic's 2025 paper on mechanistic interpretability, showing that LLMs are not black boxes and that circuit tracing can reveal multi-step reasoning and human-identifiable concepts.
This paper introduces ScaleLogic, a framework demonstrating that RL training compute scales as a power law with reasoning depth in LLMs. It highlights that logical expressiveness is key to improving downstream transfer and training efficiency.
A critique of the oversimplified claim that LLMs are 'just next token predictors,' arguing that prediction at scale induces useful representations and capabilities, and that such dismissals confuse objective with learned system.
This paper presents a mechanistic analysis of why LLMs hallucinate when reasoning over linearized structured knowledge, finding that hallucinations stem from systematic internal dynamics such as attention on shortcut cues and failures in semantic grounding in feed-forward layers, rather than random noise.
This paper investigates how fine-tuning LLMs on new knowledge induces factual hallucinations, showing that unfamiliarity within specific knowledge types drives hallucinations through weakened attention to key entities. The authors propose mitigating this by reintroducing known knowledge during later training stages.